everything is up to date it is developer ready and it has got 5GB persistent disk in it so you can store your local file and you can already see that there are some some of my folders and directories which are lying there for my demos it's a web browser access using the web browser you can run command you don't need to install anything it comes with 5GB persistent storage it is secure unless and until you log in you will not be able to access it and you will have all the developers tools which are required to deploy any applications into Google cloud platform using cloud shell so if you look at what it provides you will have shell interpreter, bash, sh linux utilities to install libraries if you want to have it gcloud app engine SDK and it's a sandbox gsutl text editors you have emacs, vm and nano buildpacks, gradle and all those these are already available out of the shell which you can just go ahead and use it and this is you can think of it's like command line interface just to go right you can go ahead and do one thing here if you come back again you can actually do the explorer file explorer and this is you can think of windows explorer like field so all your directories will be listed you can even create your own files here or modify the files here right and you can go and save the files so this is again if you don't want to play or you don't know how to use the vm or nano you can actually use this browser as well but I do not prefer this as much because I just like line x command line utilities to edit the files that's it for cloud shell guys if you have any questions on cloud shell let me know otherwise you can move to the next lecture thanks we already saw cloud SDK cloud SDK is CLI command line interface and the utility which you install on your laptop or computer let's go ahead and download it and install it let me go here I just went into cloud SDK quick start or download you can go there if you have this one cloud SDK it has detected my operating system I can go here so I already have a project you just need to download it single user mode and all you will get an option whether you want to install beta commands I said yes so installation has been done now it says complete I had to pause the video to complete the installation to save the time so let me say next finish ok so that is done let me go ahead and do cmd here and let me put gcloud so the first command which you need to do is or to initiate your gcloud command is you need to do I just put that in here you need to initialize it let me go ahead and initialize my interface so you must login to continue would you like to login I would say yes so it is asking me your user ID password to login and I just said yes allow so it is logged in as my user ID go ahead and so these are my list of projects that are available for me to select it as a default project enter now it is going to ask me do you want to set up default zone for compute I am just going to say default yes and I am going to choose us vs us vs 1a that is 30 everything is set up now let me go ahead and put forward gcloud and these are the options if I say compute instance list instances and list it will give me the list of so this is how you can install and set up gcloud command along with gcloud you will have gsutils as well the path is not set up but gsutil as well as bigquery ok so that is the utility which you will get it by default out of cloud SDK that is it for cloud SDK guys if you have any questions you can ask me or otherwise move to the next lecture thanks cloud apis this is you can think of you can use it while in your programming and it is a rest end point which you can use it let me go to the console and show you how you can explore different apis let me go back if I go to the cloud console as a home page you can go here explore enable apis and here you will see what are apis executed in your duration this one you can actually change that duration or download those transactions but let me go ahead and show you one example I went inside compute engine apis in here you will get the information how many usage for your compute engine apis but you can go and try in api explorer so here this is the sandbox you can try different apis I can go here and let me try one compute engine instance create instance list let me try this one so what it needs it needs project id and the zone let me go here and get my project id this is my project id and zone which zone I am referring and I just need to put any zone I will just give underscore so it will ask me permissions which user what it says unknown zone let me give 1a I just get the region us is to 1b it says unknown zone let me go here and see if I have any virtual machine running there it says us is to 1b copy paste so here you go the outcome the instance name what it is kind of compute instance instance 1 it is a name and it will give all the details of IP addresses and all the information so I have this IP address so api explorer is really a good tool to explore these apis and you can query it and you can get the information out and you can see the sample outcome this is what you will get it in your programming interface this is the api request which you are putting it that is the cloud api guys if you have any questions on cloud api let me know it is a programming interface that is what you need to remember if at all you want to try it you can use api explorer it is another reminder and if at all there is any questions it will be around this one if you want to try restrain points what is the option which you want to use it right and api explorer is one of the option that you will get it which you need to select it that is it as the interfaces guys we are going to get into cloud SDK installation in next section anyways but if you have any questions on theory part let me know otherwise you can move to next section thanks hello welcome to this lecture this lecture is a part of master class cloud computing for architects, designers, developers and CIS operations are you excited you should be because we are going to take deep dive into compute engine and this is the first lecture in deploying and implementing cloud solutions as a detail of service as we already know compute engine is infrastructure as a service from google cloud platform it is a virtual machine or computer in the cloud you can think of computing service is one of the three core services just to remind you out of networking database and storage and computing platform and in computing platform virtual machine is one of the three services other options available are code and container and serverless so as I said virtual machine or computing service is infrastructure as a service where you manage all the component your full access on the hardware before we dive into the compute engine let's go ahead and take a look at what is the syllabus says for understanding compute service so here you go so what we are going to see deploying and implementing compute engine resources like launching instances instance group using an instance template generating uploading SSH configuring VM for stack driver monitoring and logging accessing compute code and requesting increase installing stack driver agent and monitoring and logging other theoretical part which we need is managing VM like SSH RDP and this is more you can think of demo to understand this demo you need to have understanding on what do we mean by snapshot or images instance group all of that I am going to cover in the theory lecture here in this section going back compute engine we understand that why do we use compute engine so there are requirements which we can't easily containerize and you want to use existing VM images onto the cloud application that needs OS level changes you want to host application without rewriting it and you want to have a full control of infrastructure direct accessing network and other compute resources and that's the power of compute engine you want to spin off compute engine let's go ahead and take a look at components of compute engine disk disk are required for you to install operating system or to process the data or to store the local data in VMs images are the images using which you launch a compute engine networks we saw that there are the networks which are global in nature you need to attach the network so that you can isolate a particular virtual machine from access by someone else at the same time you need to make sure that you are accessing you have access to the virtual machine using the network you do configure firewalls and firewall you can attach it to the virtual machine as well we'll see how to do that addresses like IP address internal and external versus DNS regions and zones so we saw region what is region right so they have divided the whole geography into regions and in one particular region you have multiple zones so that if at all one zone goes down you have the zone who can actually take the request customer request so zone is physical location and how you attach or select a particular zone we'll see that instances how you manage the instances load balancer auto scaling instance template instance groups that's what we are going to see it some of the features of compute engine typically in the context of Google cloud platform it supports sub minute billing what do we mean by that so you'll be charged if at all you are spinning the instance for say 30 seconds you'll be charged for minimum one minute but then after if at all you are using that particular instance for one minute and two seconds you'll be charged for 62 seconds only right and that's the subsequent sub minute billing globally scoped images so the images which it supports is globally scoped it supports CDN content delivery network and we are going to have a demo about it disk performance you can have local disk or RAM disk for higher performance for your VMs it supports high network performance because you have Google fiber optic network which supports virtual machines live migration live migration means actually when there are maintenance happening on the physical server physical hardware then your virtual machine gets live migrated to another physical hardware but it is seamless to you and we are going to talk and look at it it supports at the start if at all some problem happened and it is not scheduled stop or you know start it will try to restart the virtual machine machine right sizing and this is a very good actually use case of compute engine that you can configure your number of CPU and GBs of RAM the way you want to have it and this is only available I think in Google if you go to AWS you have predefined set of machines which you can use it automatic discount so you have multiple kind of discounts available to you one is committed saving sustained use discount inferred instances virtual machine will talk about that in the discount section it supports linux and windows it has predefined as well as custom machine types option and it supports global load balancing it supports auto scaling and that's it so these are the features of compute engine and this will impact you know going ahead when we configure those virtual machines in the demo let's look at some of the performance attribute for compute engine and typically you know when you want to look at how your compute engine is performing what are the parameters you need to consider the first one is processor family and this is you can think of standard if at all you want you are launching say one instance in asia you know one particular zone and it has got one or two type of processor family you don't have choice so but that is this is one factor you can think of in computing performance the processor family defines whether the processor is 2 gigahertz 2.2 gigahertz or 2.5 gigahertz so some of the processor family here hash well broad well ivb hsand bridge processors the next one and which you control and you define is vcpu but typically what do you mean by vcpu what says actual physical cpu right vcpu if at all you just generalize it as a virtual cpu and if you want to map it the capacity so two vcpu which means one cpu in the physical hardware let's look at the network throughput right so what is the network throughput how actually it varies so typically the network throughput is dependent on the number of vcpus in your virtual machine and that is per vcpu you will get two gbps of network bandwidth so if you spin off an instance which has got four vcpus you will get eight gbps as a bandwidth network attached to it and you can go up to 16 gbps with eight core machines but you cannot actually go beyond that even if you increase the number of cpus after eight cores it is not going to increase the network throughput beyond 16 gbps the last aspect here is based disk hyvo how performance the disk hyvo is right and that's where you will need to think about how you can increase the disk hyvo for disk hyvo there are different parameter which you can look at whether it is ssdr magnetic disk that is one differentiated factor what size of that particular disk is right so there are these two parameter and we are going to look at what is that we said it supports two machines linux and windows and to connect spin off linux machine or windows machine what are the parameters which you look at typically the license cost if there is any like redact linux or any other license that is included in your cost per minute cost per second cost right the connection the way you do connection if at all you want to directly ssh using the console you do not require any key you just login and then click on ssh and then you will get connected you can ssh it from cloud shell you can ssh via cloud sdk and that is from your desktop or third party client that is where you need a key otherwise you will not be able to connect it but typically if at all you are connecting it from outside outside that console then you will need to open tcp22 that is the protocol in firewall if you have not opened it then you will not be able to connect your connection will be refused so that is the linux you will have a key to connect it from outside and we are going to see that in demo for windows anyways the license for windows is also included so you do not have to register your instance everything is included you will have a rdp support like ssh in linux case using any rdp client power shell you need to set the password and this is very important even if you want to connect it from the console you need to have a password to connect to the instance the firewall rule it should allow is 3389 and this is compulsory for your windows machines machine types there are let's look at 4-5 machine types in gc because there is one custom type and in custom you have flexibility to configure your own so standard machine type in this standard machine type it is balance of cpu plus memory you can think of one vcpu you will get 3.75 gb of ram and that is your standard machine and this is a balanced so this is if you don't know how you are going to use it you just spin off standard machine the cpu you want it and memory will be configured for you and you can go from one cpu to 64 or 96 based on your need in standard the second one is shared core and this is very small when you have very light load and you can think if you look it here if you take 0.2 or 20% of your cpu in f1 you will have 0.6 gb which is 600mb of ram and you can attach a disk the way you want to have it but this is how you represent f1 machine and then you have g1 machine in that you have 60% of cpu it's not even one vcpu and 1.7 gb of ram micro busting and what that means if at all that particular physical hardware has a capacity and your application or the programs need additional capacity for some point in time when your program is running at that time it can go beyond your 0.2 cpu usage to say 0.3 or 0.4 we don't know that but that is that f1 allows micro busting for certain loads and the next one is high cpu machines and this is where you have higher cpu as compared to memory if you look at the standard one it has got for one cpu vcpu you have 3.75 gb of ram but in case of high cpu you will get for one particular cpu you have because you do not need that much memory so it is 0.9 gb and you can go in multiples of that in this kind of machine the cpu starts from two and onwards in standard it can actually you can start with one and onward but in this machine you can actually start from two and onwards and there are some restrictions you cannot go below one gb in these standard machines and that is why they have I think restrictions high memory this is actually the vice versa of high cpu wherein you have for one cpu you have 6.5 gb of memory and this is where you you are processing too much of data in ram and that is where you need in this case also you will have cpu from 2 to 64 or 96 based on your need and based on what you have availability typically the machines are represented like this if at all you are using the standard ones but there is the third one third machine type wherein you can actually go from you can define your own cpu and you can define your own ram right you can mix and match between all these configurations right whatever it is available and there is a there is actually button here and you can extend the memory as well and you can go up to 624 but I'll show you how to do that I just wanted to actually show you another one this is ultra machines and they recently came up with this particular machine type in these ultra machines you have vcpu 40 and you can go up to like 961 memory so these ultra machines are special requests you can think of and you can use it they are they must be very expensive if you look at number of disk you can attach it any of the machine any of these machines or ultra machines you can go up to 16 persistent disk attachment and up to 64 terabytes of total memory which you can attach it to any machine let's go ahead and look at this in console how does it look like let me go here so if you want to spin off virtual machine just go to home and compute engine and the first step click on create you don't have to spin off the machine to explore so you can provide the instance name the way you want to have it the region this is where you specify which location you want to launch it I'm just selecting say by default region here default zone here I'm not changing it and you can see your monthly estimate for whatever definition whatever configuration you have it right and this is where you can go ahead and change and define your configuration so either micro small which is G machines right F and G machine and then standard machine if you go down high memory machines high memory high CPU and ultra machines right this is I think this is the new machine they have it like mega machine mega memory somewhere like 1.4 terabytes of memory with 96 vcpus but this is ultra machines and just keeping whatever I have it you can even customize it that's the third one right so you can define like I need 4 cpus and then I need say 24 and this is based on your calculation right 6.5 gb per cpus and that's how you have 26 gb but you can just click on this one and now you can go so much high 624 I just captured that particular number from this itself as I said you can even use or configure cpus platform right let me go here basic flow first one and cpus platform automatic and this is where you know you can map cpus platform and that will give you the speed right which I mentioned so if I go here so it depends on the cpus platform you allow speed let me go to vcpus okay I don't see any configuration here how the network bandwidth impact but that's the return one let me go other aspect so if you want to attach gpus you can attach gpus and you can I didn't tell you can choose which model you want to have it I'm just saying none here right now then if you want to deploy container we'll talk about this in next section and then service account on default service account I have service account which I can just use it app engine so I have these three service accounts and just keeping it as a default when you log in or create virtual machine when you enable the api then you will have service account which is by default then firewalls right you can add firewall tags I don't want to say anything here if at all you want to have HTTP traffic you can do that you can add firewall rules if at all you want to have it you just need to click that details page that's where you can do management you can just give some tag labels automatic startup script if you want to say run one particular script when you start the instance this particular script will be executed if you want to say start apache instance or tomcat instance in the back end you can install you can have the startup script here we'll see this in demo ok meta data you can assign meta data to the instance primitivity on and off you can do it I'll talk about that later automatic restart when machine isn't maintenance mode this is where you can switch on and off if at all multiple if at all for some reason hardware failure or any any reason right your machine stop you can set it automatic restart on or off and host maintenance what you want to do you want to actually live migrate your virtual machine live migrate means you migrate your instance from one particular physical hardware to another one to stop it somehow magically google does it you will see some performance bottleneck if at all hitting you are hitting like 80% CPUs but if at all you are using say 10, 15 or 20% of your CPU and mam memory you will not feel the downtime at such and that was management if I go to other tabs like security If you want to encrypt your data, you can, if you want to secure boot, you want to use TPM or Integrity Monitor, you can configure it here. We'll talk about all of this in security section. The disk, as I said, you can have the disk and that disk, that disk, you can have the opening system installed or the database installed. You can go to networking, you can attach the networking here. I just have one particular networking, so I don't need to worry about that right now. Sol tenancy, we'll talk about sol tenancy, but sol tenancy is like, if you go here, just understand sol tenancy, what do you mean by that, right? This is a physical box, physical hardware machine, right? Sol tenancy means you will have your, your virtual machines running in one particular hardware and no one else is actually using that hardware. That is what, so this particular server is dedicated to you only and that's the sol tenancy. Go back, okay. That's it as a basic. I will continue on to compute engine, the next aspect like GPUs and everything in next lecture. Thank you. Let's look at quickly what I'm going to cover in this demo. Server balancing, auto scaling, high availability. This illustration is something you can see in coming versions of your lectures wherever it is applicable. Consider this particular representation wherein we have website and that website is used by users across the US. I'm just considering the US use case and which you can ultimately replicate it to the different regions across the globe. You have the subscriber base which is located in east. You have subscriber base or users which are in central. You have users which are in west and then cloud DNS which takes your request forwarded to the load balancer which you can see HTTP load balancer here. And then the request gets routed to your implementation and your implementation could be container engine, app engine, cloud function, probably your compute engine and considered here only the compute engine part because individual implementation like container works differently than app engine than cloud functions. So consider a plain simple case here. Assume that all the instances are the compute engine instances and understand how load balancing works. All the requests which gets into the cloud DNS are forwarded to the load balancer and load balancer distribute those requests across multiple instance to service those requests. Load balancer makes the decision where to forward those requests to the backend services. When the users from the central wants to use the service or website here, the request goes to DNS, DNS forwarded to load balancer. Load balancer magically makes a decision and understand this particular customer is coming from US central and it should be served from the nearest location for him and which is like the implementation which is based in US central and the requests are forwarded to the compute engine which is in the central. Consider the next case where the users from the east trying to access the website or the service, the request gets into cloud DNS, gets into load balancer and those requests are forwarded to the nearest region or zone for that particular user. In case of east, it should be somewhere in your east of US. Similar fashion, if you have the customers actually accessing the service from west, the request is forwarded to the west implementation of your services. So this is how cloud load balancer makes the decision and forward the request intelligently to the backend service. But hold on cloud load balancer use another parameters or input to make this decision. Let's look at this. So you can see consider looking at you know the speed of these particular requests which are getting into the different services in different regions. You see there are considerable amount of requests from west to the west compute engine. So as the request are continuously going high, the server is getting busy and busy and busy and that's where auto scaling will play important role. But hold on here is the catch. If load balancer checks a particular container engineer is somewhat full and they can actually then it needs to be reduced the traffic from those particular instances. It will route to the next available instance or the region which is you can say some of the request are which are red dots. The requests are getting into the US central as well at the same time. In the meantime, auto scaling makes the decision and decides that they need to create another instances because there is considerable amount of traffic and it kicks in additional instances to serve that particular traffic in US west zone. That's the auto scaling. And then magically everything works and then the additional instances also start getting the traffic from the US west users. So this is auto scaling plus the cloud load balancers decision what to forward and where to forward the request. By this time, you know that how load balancing works and auto scaling works. Now understand how what HA high availability. So consider a case here if something goes out, right? If you are not able to reach to your central and just replicating or using the regions concept here, but you can think of central region is one particular zone. Now the link to the zone is gone or because of some reason it is not available. What will happen is your request which are you can see the yellow dots which are going there now cloud load balancer with the health check understand that there is no connection to the central zone. And then it makes a decision to forward the request to some of the instances which are available to or near to them. And in this particular case, it is east because west is somewhat far for them to send the request. So it will send the request to east and that's a check. So you understand cloud load balancer makes a decision where to forward the request. It has a health check which checks if the instances are healthy to send or forward the request. It also checks whether the instance is not too busy to service it or otherwise it will forward it to some other location which is nearest to them. I hope you understand in a high level how load balancing auto scaling and H.U. works. Let's go ahead and create some virtual machines to understand how we can actually go ahead and create those. This is Google console to have this. Let me actually just get into the console straight away. Once you get into the console, you will see the dashboard. It will show you the projects which is currently selected. You can actually see multiple projects whatever you have created in this particular account. You will have this information. You can actually see if there are any resources which you have created that there are known right now. So you don't see any. There are multiple actually documents or you can say guide. You can actually use this guide to create the virtual machines straight away. You can see the API's traffic currently which you are using and there are different items here which we already saw it in the console's overview. Let me go straight to the Google computing gin. So this is what I have right now. I just cleaned up each and every resources in this particular project to create fresh. Let me go ahead and create one virtual machine. So you can actually see that it will need instance name here. I can actually type in any name which I want. So you can actually see that it is only lowercase letters, numbers and hyphens. So let me put web 01 instance name. The zone as we discussed earlier ultimately all those data centers are divided into regions and ultimately the zones. So if you look at there are multiple zones here wherein I can go ahead and create as we are actually talking right now. These are the zones which are available. So the first later you can actually see that Asia east one that's the region and then there are multiple zones in east one which is A, B and C. Likewise we have multiple zones. So the regions and corresponding zones associated right now which is available for this particular resource to create. So let me just you know pick any of these zone and you can actually see the price and these are estimates for monthly you know the virtual machine usage. You can actually go ahead and change different zone and you will see the price is also getting changed. So you can actually very well get that the price for this particular resource which is instance virtual machine is getting changed or is dependent on you know the zone which you are actually creating or in fact it is the region which you are creating and then based on the region the pricing will get changed. So which says effectively hourly rate you know 3 cents 3.4 cents. Let me you know let us try to search a very cost effective option if we can get. So you can see in Asia it is $28 per month in Australia is $34 which is somewhat expensive in Europe 27 Europe West okay $31 US Central 24 East 27 and West 24 again. So East or West you can actually just go ahead and create. You can actually select the machine type which you want to create. So you can go ahead and select the available machine types. As we discussed already there are multiple machine types which is like first one is the standard per CPU there is 3.75 GB of memory available and you can go up to 64 virtual CPUs and up to 240 memory in the standard. Then you have high memory in this particular in this particular machine type you have per CPU there is a 6.5 GB and you do not have option to select one virtual CPU you need to actually select two or onwards or more and you can go up to 64 CPUs and 416 GB of RAM. And then you have high CPU machines which are you know per CPU there is 0.9 GB of memory which is available. So and you can again cannot select one virtual CPU here as of now you need to select at least two to go ahead and start those instances you can actually see you know you can go up to 64 virtual CPUs with 57 GB of RAMs. You can actually select all those configurations or combinations based on your need whether you need more memory to cache some data or you need more processing or the CPUs to do say computational analysis or something. So based on your requirement you can go ahead and choose but there are besides these there are other two which is shared CPU the CPU itself is shared between the instances and you have memory options between 0.6 GB to 1.7 memory one is f1 micro and given small in a f1 micro we saw that you know using f1 micro you can actually go ahead and so the instance itself will be you know it can throttle to the higher speed based on the requirement. Let me go ahead and choose say f1 micro for that 0.6 GB memory is already selected right. Let me just go ahead and customize this one and see what I can get. So if I come out of virtual CPU which is one it is giving me 3.75 GB which is standard machine and yeah I cannot actually go below 1 GB or I cannot go below 6.5 6.5 is the machine where you have high memory machines you can say option. So it will get you know increase you can actually select any combination of those. The best thing about this particular combination is you can very well see the what is monthly cost what is estimated monthly cost definitely if you go ahead and terminate these instances and then use it again or stop those instances for certain time and use it again there will be progression happen but this is high level running cost for the machine considering 730 hours per month. Then you have option actually to select based on the CPUs and the memory let me actually go ahead and select you can actually go ahead and select the the CPU platform okay. So you cannot actually I think select the CPU platform if you are in custom yeah but let me just go ahead and choose one of these particular machine and try to select because there are constraint and which you cannot use certain platforms okay. So I think in US West let me choose US Central let me see what platforms yeah. So it is not actually based on the CPUs definitely there are constraints to select the CPUs but it is locked to the zone that particular zone if you have the CPU platforms available like Skylake, Broadwell or Hashwell that is available for US Central US is let me see what are options only one US West that is what we saw only one. Let me go ahead and select Central and if I select say Central for 7.5 GB RAM to V CPUs and I want to select Ivory Bridge the price for those two or any of this platform is going at least currently showing it is same for me I can go ahead and add GPUs. So GPU aren't available in this particular zone only you selected I need to actually change the zone let me choose US East okay that's not there in US East as well okay. Now in US West you have option to choose GPUs and I can choose two GPUs of this one you can say the cost is going tremendously high. So if I take out GPU it's showing somewhere around $50 but if I choose any of the GPU the cost goes too much high somewhere around $500 per GPU is what they are charging per month right. So let me actually not select this one right now about this this is where actually you can choose predefined images if you want to use the public images right now you can actually choose it from the images and the options available as I said Windows and Linux whatever it is used right now in the market you can actually choose either the Windows machine or Debian Linux as an example right or you can actually choose any of the any of the image application image like this one is Windows Server 2012 or you can actually select from any image I don't have any image right now I cleared everything I don't have snapshot I don't have existing disk at all to choose the image from the boot disk actually you can you have a choice to either select the magnetic disk or SSDs and price actually changes based on what you choose it and in fact this speed is associated to that gets changed. Let me go ahead and just keep the magnetic disk and continue select. So that's the Debian Linux 8 which I have selected it is asking me service account I want to actually select the same thing I don't want to actually do too much ifs and buts I want to select all HTTPs and let me go ahead and with all defaults create. So this will take I think some moment to get that particular server started yeah the instance is ready now and there is a so there is internal IP address which you can actually use it for internal load balancer or any other internal communications of rules for external IP that's where you can see this IP is assigned and you can actually very well go ahead and ping that particular IP if the traffic is allowed you have multiple options to actually click connect to this particular instance one is through your SSH browser right now the way we are doing it or there are multiple you can even connect it with party for party there is another you know a process will top that in detailing subsequent lecture but this is the way let me just go ahead and connect with SSH see it is connected you have full access to the to the machine right now if you want to actually do SU which is root access you can just very well go ahead and do it so that way you can access I will see more features on this one in subsequent lecture so if you are ready let's go ahead and jump on to the next lecture thank you in this particular demo I want to create or show you how to create Windows virtual machine and how to connect the instance so if I click on create instance I can see web win 01 I want to create that in west because it is somewhat cheaper I want to change this image to the windows one thing to note here is if you select some of the windows machines it will show you you know the size for the persistence is 50 GB and for some other it is it will show you you know for some other which I tried I want to create the it is 32 GB that is even fine so right for some other it is saying that 50 GB is required so at least 32 GB is required for some windows instance that is the take here I am just selecting whatever it is required but I want to create the SSD for this particular instance because it is taking too long time to create the instance and I want to even change this particular instance for the demo purpose because it was taking too long time to create the instance with the small configurations so I want to allow everything here so SSH key actually it is you can say similar process but I am not creating any SSH key here or attaching it I want actually prem table so if I switch it on the price should go down drastically so before I switch it off it was 112 dollars and if I switch it on now it is showing me 78 dollars this price is there because I have relatively considered big machine if I select a smaller machine it is 41 dollars let me choose the big one because I want to create this video as much as fast I can do it so let me create it so the instance is being created let me pause the video for some moment so that will come back once the instance is up and running so the instance is ready so it has got external IP address internal IP address I can go ahead and create instance group out of this particular instance I just let me just go ahead and do RDP to the particular instance it is asking me password I have password already created but I think it should reject because this particular password which I had created was for the other instance so before you guys go ahead and connect to this particular instance you need to reset the password for this particular instance so set password for this instance so this is the user and this is my password I can use this password to connect it but keep this in this is in mind it takes actually some time for you guys to get this particular instance if the instance is very small you need to wait for certain you know at least some time for your password to get reflected in that particular instance because it takes some more time so the instance is coming up now or it is at least getting connected you have option to either disconnect minimize send out control this is this is Chrome's you can say extension for RDP which I am using it right now I have created this particular instance but this is not you know unfortunately which I created with the desktop so whenever you do RDP you will be able to connect this particular instance but you will not be able to see the full desktop here so I have created another instance which we are going to do RDP in a short while once the instance is ready I will pause the video for some time so the instance which I have created earlier was the server instance and it does not have the graphics inside it so in the meantime what I did is I created another instance Windows instance let me reset set the password for this one as well copy the password just need to actually save this password you need it while you are connecting it so close and do RDP to this instance it is connecting so it takes while for smaller instance to get that password reset whatever we did either you can actually wait for some time to get that reflected or you have another option actually just to go ahead and stop the instance and start again so that way you will have all the updates for password into the instance so the instance is coming up so the instance is started now I have resetted the password as well and I have the password for this particular instance let me do the RDP so I think there is some issue which this particular guy is guiding me on certificate and the instance here is 97 delete certificate so connect again password continue see it is now connecting to my Windows instance which I have created the earlier instance which I had created was only the server version so it has got no actually the UI as such as a desktop I have created another one with the UI choosing another image so let us go ahead and understand if this particular instance has got a internet connection right now or not once it pops up first time usually actually takes time it will not be too much for the second time onwards because for Windows it has to set up the profile and everything when you are logging in for the first time so this is internet explorer okay and this would be the risk so let me put google.com continue I hope it is coming in this is warning message here see we have google.com so let me actually try to actually install Chrome here I do not want actually this warning message to come up so I want to go ahead and download Chrome let me see if it is allowing me to install that here and you can actually configure the Windows option here if you want to do it taking some more time probably the instance which I have chosen is this guy is still setting up the options I think let me go ahead and pause the video here yeah means ultimately I can go ahead and browse internet so which is good news and then by default google actually gives you google cloud you know the shell you can run gcloud commands inside the machine if you want to right so that is it for I think the Windows machine guys if you have any question on this one let me know otherwise continue to the next lecture or the demo into which I will try to explain so that is it for the Windows virtual machine guys will continue our discussion on the virtual machine demo and next chapter is the next demo for us hello welcome to this lecture this lecture or the section is part of master class cloud computing for cloud architects designers developers and services operations our main focus is Google cloud platform and certifications around it are you excited to get into new section altogether yeah yes we are getting into data solutions and this is the next one after completing our computing service database and storage services one of the three core services that are available in any public cloud platform and it contains relational database it contains no SQL databases like Hadoop Dynamo DB MongoDB Spark Cassandra and what not right so that is all database and storage service database and storage service along with AI divided into three different high level categories and this division is because of we have new partners like artificial intelligence and big data and those are like offerings on to public cloud platform and that's why we have you know so database and storage is again one of the three offerings that we have in space of say data big data and AI so database and storage contains SQL no SQL database and storage we have big data offerings like Hadoop Spark Apache Beam like that and then we have machine learning and this is altogether separate topic altogether but we are going to look at machine learning in actual as well so what is that we have on database and storage services we have cloud SQL which is relational database on cloud and you can actually launch my SQL as well as Postgres SQL instances in cloud using cloud SQL out of Google cloud platform cloud spanner it's homegrown relational database management system horizontally scalable across the globe big table it is you can think of HOS implementation in cloud Google cloud platform data store you can think MongoDB like implementation in Google cloud it's a document DB and then we have cloud storage it is an object network attached storage you can think of you can just store an object into this and the object could be any static file or images or videos or like that and then in big data service we have cloud BigQuery cloud Dataflow cloud Dataproc Datalab and PubSub we are going to see all of these services together in fact cloud PubSub should not be a part of big data service they have just categorized it inside big data service it is an integration tier or messaging platform which we use it for all our messaging needs in public cloud let's get into details of what it is expected out of you as a cloud engineer from from data site right so you need to deploy and implement cloud solution around data space in short successful operation of those cloud solution around data services when we say you know deploying and implementing data solutions in nutshell what they have put forward is initializing data systems with the product like cloud SQL data source data store BigQuery cloud spanner cloud PubSub all these data services like initializing so you need to launch instances or initialize the service for you to use it and then you load the data using command prompt API transfer import export from cloud storage streaming data to the cloud PubSub and this is wherever it is applicable okay this is the part of section three deploying and implementing solutions and as a part of section four managing data solutions onto the cloud you need to execute the queries once you have the instance ready everything is there in the cloud now you need to execute the queries retrieve the data from data instances on all these estimating cost you cost of BigQuery backing up restoring data instances reviving job status in cloud for cloud Dataproc and BigQuery moving objects between cloud storage buckets and this is pure in the context of cloud storage converting cloud storage bucket between storage classes setting up life cycle management policies for the cloud storage working with management interface in cloud console so all of these are the details syllabus but if you summarize and categorize all those things together what it meant what we are looking at is for cloud SQL data store big table Dataproc and spanner we need to launch a new instance create new instance set up those instance or configure using different parameters load the data to and read the data from those instances high availability configuration backup replica and restore configuration all those are like admin activity and you have to manage it in the cloud environment for cloud storage your managing buckets objects etc. your managing storage classes and its pricing around it how you optimize your cost if you look at BigQuery and Dataproc so BigQuery just manage service how you schedule a job and execute a job the process in its life cycle for Dataproc it's a Hadoop and Spark implementation so how you create Hadoop jobs and Spark job and run it and maintain its life cycle for cloud Pub-Sub it's a messaging platform and we will get into demo of this one we just had a glance of it in computing platform as well but we are going into details of cloud Pub-Sub and to create a message and trigger those messages data flow it's purely you can think of not asked especially in the in the syllabus but we are going to cover so that you understand if at all you have any questions one or two you should be able to cover that but we are going to go through that that's it as a starter Kickstarter for this particular section if you have any questions let me know let's get started on to cloud SQL thank you Cloud Spanner it's a global RDBMS or relational database management system which Google has built it from the ground Cloud Spanner is one of the database service that are available for us to use it and it's a RDBMS category so the database and so it is a part of database and storage service Cloud Spanner this one so you can think of we saw cloud SQL which is a relational database management system and that using which you can launch MySQL a PostgreSQL instances with Cloud Spanner it's not you are not actually going to launch any other database it's a Cloud Spanner is the database which you have it and you can launch it you cannot take Spanner on to your on premises environment so you need to just launch it from the Google itself currently Cloud Spanner is used as a Google's own RDBMS requirement and it is globally scalable database that we have available so Cloud Spanner it is fully managed relational database management system built from ground up and that is done by Google all RDBMS requirement and that is the Spanners you can think of use case ad take financial global supply chain and detail you can scale plus consistency requirement and that's where you use Spanner it is horizontally scalable database that we have available and we are going to see it mission critical applications with high transactions but at the same time you can say if someone has global requirement global database requirement that's where you can use Cloud Spanner besides cloud SQL that we have typically what is the best of you know best of both word that Spanner is giving it to us it has got a schema which is there in traditional RDBMS which is not there in the relational database you have SQL like syntax almost all the SQL syntax which is there from relational database is available in Cloud Spanner which may not be available in some of the no SQL database consistency strong and consistency which you can tune it in Spanner and in relational database where in no SQL you have eventual consistency availability in terms of availability is high available horizontally scalable and automatic replication whereas traditional RDBMS like MySQL Postgres SQL or Oracle you cannot actually do horizontally scalable it is fined to fail over you can actually scale vertically you cannot do horizontally a replication it's configurable the way we want to choose it definitely a no SQL provides high availability as well as horizontally scalable as an example I can give you you know the dynamic DB or MongoDB or you know Cassandra it is highly available clusters that we can use it some of the Cloud Spanner use cases and this is you can think of the use case or typical use case which you want to understand why do we you know use Cloud Spanner this is not mandatory for engineers Cloud engineer certification but it is required for the developer exams and cloud architect exams so financial trading inconsistency leads to and this is before Cloud Spanner inconsistency lead to potential monetary loss during reconciliation global reconciliation replication is a trade off feasible inconsistency lead to incomplete view of a customer and you can actually map this to the the type of database which we are comparing is you know Oracle is there say as an example Oracle is installed in Europe somewhere right that is not available in all other so the instance is there in the in the Europe and it is not available in US and Asia right whereas Cloud Spanner actually it can span across multiple regions and you can have strongly consistent database spanning across the globe and you can maintain this consistency you can have unified view of the customer and you can actually compare that so all of these thought processes are the use cases which you have it is in comparing the database which is which resides in one particular region or location whereas the database which span across multiple regions or locations global call centers event eventual and out of touch it's a real time and up to date supply chain management and manufacturing supply chain manufacturing supply chain presence inconsistency global view or data must be shipped in batches and this is where you if you want to actually reconcile the data between all other different locations then you want to reconcile or transform the data into one system and then that's where you get the single view with Spanner you can have global real-time consistent view of enable which will enable real-time decision-making telecom and billing processing capacity limited to finite scale up compute resources scale out allows improved processing speed and I don't think this is a problem for many of the telecom providers because I work in telecom but maybe some of the telecom provider may face this logistics and transportation regional reach with many systems glued together global reach to lower latency and consistency view gaming each server and cluster is own universe like you need to have different cluster in different regions to provide consistent and fast access to your gamers consistent global will to deliver unified experience e-commerce limited availability SLA or no SLA guarantee in practical practice potential missed sales guaranteed max up to five minutes downtime on paper like this is the SLA which they have it this is you can think of it's like sales speech or why even summer Spanner exists today okay in a shell what what you want to see is the one is regional in nature or zonal in nature if you map this to the Google cloud or any public cloud context and Spanner is global in nature so these are the attributes which are there I'm not going to get into details what is valid and what is not valid and why it is not valid okay this is just information purpose I'm going to share the slides and you can actually go through it global scale it is here a bite scalable and practically infinite the way you want to have it availability monthly SLA multi regional offers 99.999 with downtime less than five minutes and this is monthly SLA consistency purpose to build for external strong and global transaction consistency and this you can think of why they are putting forward this consistency right so they have this fiber hypha you know the network which is actually backbone for the Spanner and all other Google services and the way the speed of the network which they are achieving it they are saying that even though this database is globally nature it is still strongly consistent database in 2018 what we saw multi regional admin versus data logging strike driver integrations all of these are like in 2018's enhancement you can commit timestamp you can have commits time time stamp time stamp compliance requirements which you can enforce it so some of the data which are like European Union it can reside in Europe and like that open census integration you have import and export out of Spanner DML queries also supported and query stat also supported so all of these enhancement is related to 2018 and that's what we saw in earlier when I prepared the other codes this was not there you can think of how this the global you know global Spanner look like right if you look at if you create a database and the database actually as an example here DB one right so DB one existing zone one it is existing zone two and it is existing zone three as well like DB three it exists in all three zones DB two exist in all three zones like that so it can span across multiple zones that is what we want to say here the instance is typically if you look at if you drill down the DB one part right if you look at DB one exist table one and table one is there in all three zones table two is all three zones so consider a case wherein when the update actually goes to say zone two right in this particular table table one what it will do is it will take the updates right into database one but at the same time it will hit the other two zones right it there and then so then you will get actually feedback back that the confirmation back and then that's how you get the confirmation for the commit and that is how it is strongly consistent database so consider if at all zone two goes out for some reason what will happen is you can still go ahead and get the data from you can still go ahead and get connected to zone one and that's how the data gate actually data goes to zone one and then it will write to zone three and then you will get the confirmation back so what is instance instance is allocation of resource when you create an instance you can choose where your data is stored and how many nodes you use it for let's go ahead and create one instance in cloud spanner so I'm in storage and inside storage you have this cloud spanner option so cloud spanner is fully managed machine critical relational database I'm not going to read this you can just go through it what you need to give is you need to give the instance name jcp train cloud spanner instance and you can actually configure regional versus multi-regional nodes the configuration you can choose it if you say multi-regional then it will give you only few options right europe uh num your ishia and these two three and six says I think us and you will get the details about it right northern virginia southern virginia like that so two read replicas which are available in us east four and two read replicas which are available in us east one that is south carolina availability is this so whatever you choose it here you will get the details out of it so num six and all of these the details about it uh if I choose europe you can see belgium and netherlands uh you will have this region and then there are two redux read write replica in europe west and two read write replica in europe west four if you click regional and select one particular region you will get the information right regional asia east one three read replicas in three separate zones within europe ish ishia east one availability is reduced so if you at all you choose any of the regional ones you will get four nines of availability if you choose multi-regional uh example this one you will get five nines of availability finance of availability means uh it is 0.001 percent not available right which means around five point four or some something like that minutes in a in a particular year the instance may not be available and that's the s le guarantee which you will get it i'm going to select regional and you can actually just go ahead and choose one you can define nodes here okay and the nodes is uh the cost factor so if you increase the node here like you will get 400 nodes you will have 90 dollars per hour charges storage 30 uh gb per per month okay if i change the number of nodes the node cost in terms of processing gets changed but a storage remains same because you are using the same storage node guidelines right what is the guideline which you have so each cloud spanner node in this configuration can provide up to 10 000 qps and that's how you request for a node if you have only one node you will get 10 000 qps of read this is queries per second that is read queries per second or 2000 queries per second of write so this is r it's not actually and so mix you can actually mix and match up all these queries and that is what uh you will get the performance on the writing single row at one kb data per row and two terabyte of storage so you are actually pulling the data from two terabyte of storage and that's the per node performance which you will get it you may ask a question about you know you have high your database is suffering and currently database has got two nodes uh what is your recommendation whether you want to increase cpu memory what is that you want to do right and your answer should be the nodes if the node is there for optimal performance of the configuration we recommend provisioning enough nodes to keep overall cpu utilization under 75 percent minimum three nodes recommended for production load okay so there is nothing here at search i can just go ahead and hit create and it will create a spanner uh for me so there is no data yet so spanner instance is created for me okay i can create database inside the node i can give gcp spanner demo db spanner db continue and this particular name is unique to your gcp product project define your database schema at the tables indexes the way you want to have it edit as a text you want to you can actually give statement to create it i'm just going to create a database so now i have a database so instance i have instance inside that i have a database i can create multiple database inside it i can say create multiple database i can edit instance i can see instead of two i can instead of one i can put forward two and then cost will be changed like this uh you can import the data you can export it out of it you can either delete the instance okay so that's the cloud spanner in nutshell this is how you launch uh the instance so when you create an instance you can choose where your data is stored how many nodes you want nodes typically individual nodes that's what we saw spanner has node to process the data actual instance node provides 10 000 queries per second rates and 2000 queries per write with single one kb and this is assumption if at all your size goes high then probably you'll not get this performance and the performance will be reduced uh and two terabytes of storage node count there defines the number of nodes in the instance for additional performance you can add nodes encryption it automatically encrypts all your data before it's actually writes to the disk data is automatically and transparently decrypted when you read from the authorized user there is a concept of uh interlude table here uh and you can actually have one table sitting inside on the other one like the example here it does not really matter for you uh in in here i can have additional data is designing for spanner as a separate lecture but it is not really uh required here uh this is the way you can actually go ahead and create the tables uh so the table has got uh fields and fields has got a type and then you can use primary key you can just go ahead and get any select statement from create statement from the internet i'm not or i can actually just go ahead and copy paste this and create a resource file so that you can just go ahead and create it okay uh so first one is just a table single table uh second one is actually the album table and which sits inside the single table and you can have a songs table interlude a parent album and you can go ahead and create the indexes also so indexes is also supported you can create uh index on the songs with the multiple it's like cascade index so the number of spanner nodes in your project is based on regional or multi-regional the amount of storage that your table and secondary indexes use the amount of network bandwidth to which is used and this actually translates you can think of the number of nodes right it translates to per node our rate regional you will have 90 cents and that's what we saw to 1.126 depends on the location which you choose multi-regional it can go from three to uh you know nine and you can actually go here i'll show that create instance if i go here choose multi-regional and select any region then you will get you know three dollar for asia it is more nine dollar per hour okay so it is different in different region so three five and nine dollars okay uh for the storage uh per jb per month 30 cents to 45 in case of regional 50 to 90 in case of multi-regional if you look at here multi-regional this is 70 cents if i choose uh change the reason it can come up to 50 cents or it can go up to 90 okay in case of regional it is different so uh you will be charged based on amount of network traffic or bandwidth which will be used uh ingress is free egress with the same region is free you need to keep that in mind between the regions you will be charged per jb uh one cent intercontinental egress at internet egress rate and you need to look at you know the network uh the network charges which will be applied in uh the vpc and the network section clouds panel and iam as because it is ground up build uh it is very well integrated in iam and you will have different uh kind of iam permissions which you can use it one is instance configuration you can create a list get the instant information uh the second one is instance operation uh you can delete uh create and all that instance operation uh you can create a database this is inside the instance you are creating updating database you are getting the database you are doing it database operations or you can even you know you have permissions for session like whether you can connect to the instance or not right or database or not so typically admin is a person admin database database admin is again a person or database reader users can be machines and viewer again can be a person and this is primitive roles which are also used uh that's it guys i can actually go ahead and get another demo created wherein we will go ahead and create the database tables as well but thank you for this particular uh but that's it for this particular lecture guys if you have any questions let me know otherwise you can move to the next lecture thanks cloud networking cloud networking is very much or you can think of critical uh service that are available from any cloud providers right now there in the market we use cloud networking to isolate your resources cloud resources from other companies or from public accesses let's go ahead and get into details of what are what is that that we have available from google cloud platform cloud networking is one of the three core services that are available from google cloud platform or any cloud platform to say in cloud networking you can create virtual private cloud you can have load balancers you can create firewalls routes sub networks cdn as that you are actually using it from your own data center in a high level cloud networking divides into three different areas the first one is the load balancer that's where you take the traffic from your customer and distribute your traffic to the backend services and we saw this in detail in our computing service the second one is vpc and that's where you create the virtual private cloud which is you can think of private networks in the cloud in the global cloud and you can spin off any resources inside that particular virtual environment or private environment so that others cannot actually access it so vpc is one and you have subnetworks firewall and vpc created inside the vpc to isolate different environments but besides that you go ahead and create a hybrid connectivity like if you have your data center and you have some applications running inside your data center you want to connect your data center with the google cloud platform and that's where the vpn or the interconnections part also will come and that is what we are going to see it in vpc as well as vpc as well as vpn or interconnections in this topic we are also going to see dns and cdn services these are also the core services that are available but those are you can think of optional service or additional service which you can use it based on your requirement but we are going to see this in detail in this particular section so in nutshell we are going to see cloud vpc which is the network inside the cloud we are going to see interconnect interconnect means connecting your data center with the google cloud platform we have already seen load balancer we are going to see cdn and we are going to see dns here in this particular section cloud vpc it is managed networking functionality for google cloud platform resources using cloud vpc you can create a private network what it means is you can provision your gcp resources connect them with each other inside the vpc and isolate them from one another using or creating different vpc or the subnet inside it you can also define fine grained networking policies with google cloud platform on premises or other cloud infrastructure using vpc you can think of it as comprehensive set of google managed capabilities including granular ip range selection you can define routes firewalls and cloud routers as you are actually doing it in your own premises or your own data center let's look at some of the features for vpc it is built you can build private global data center like field without managing hardware in terms of like hardware switches or routers or building it on your own you can have subnet firewalls route or you can define the pairing inside vpc you can monitor the network connections using flow logs and it is global shareable and expandable by design so you don't have to provision any resources it is not a physical device so that it gets bottlenecks when there is a problem or there is huge bandwidth utilized it is managed functionality everything is managed by google cloud platform and it scales based on the requirement so you don't have to worry about the scaling it is software defined and it is not the hardware i just wanted to reiterate that it is possible to provision cloud resources you can connect them with each other and you can isolate them you can create a subnet and you can even isolate different environment like prod dev and test right and that is what vpc provides different there are different types of vpcs which are available one is the default which is created by default when you have an account you can create auto mode vpc or you can do custom mode vpc and we are going to get into details of that some of the features of vpc in google cloud platform it is global in scope so it is not specific to region or zones or data center right it supports multi-tenancy you can have private communication you can define subnetworks you can define firewalls you can define routes you can have cloud router for bgp link you can share the vpc you can have access control managed via iam so in nutshell vpc is a comprehensive setup you can think of google managed networking capabilities including granular ip address range selection you can define routes firewalls and it is virtual private network on the cloud and it supports cloud router and what what do we mean by cloud router we are going to just see it in the next slides so vpc is a virtual version of traditional physical network that exists within and between your physical data center so you can think of it is virtual version and it is software defined and it is not your physical devices inside your own data center each gcp project contains one or more vpc network vpc is global in nature and allows global vm instances and other resources to communicate with each other via internal private ip addresses so vpc itself the concept of vpc itself is global in nature it is not tied to a particular region or zone so vpc does not have ip range so ip range is created within the vpc you can create different sub networks so part of that particular network and you can attach the ip ranges to the sub network so if you look at the vpc it is firewalled network and it has got no ip ranges it contains sub networks and sub networks has ip ranges you can have more than one sub network in a region for a given vpc as an example if at all you are deploying your services into say us east one of the region you can have multiple environments created they are like dev stage performance testing or uat and the production you can create all of those as a different environments or different sub networks and that's the power of sub networks some of the thoughts on project and vpc all the objects associated with project definitely because vpc is contained within a project so all the objects inside the vpc is also associated to a project each project starts with default vpc and that is default which is created out of it you don't have to create it and it is in automated so everything is created for you you can go up to five networks per project and this is the limit right now that we have it to create the vpcs let's go ahead and get into details of different type of vpcs that are available and its features the first one is default vpc so this vpc is created and already there when you have a project so for each and every project you have default vpc subnets are created by default per region per zone internet gateway also created firewalls also open between subnets so that all the resources can communicate with each other let's go ahead and browse this in the console so if you look at if you go to network and go to vpc network options you just click on vpc network and this is the default vpc which is created for you you don't have to create the vpc and these vpcs contains subnetworks that are created per region all the subnetworks are created the routes are defined for all those subnetworks so that it can talk to each other and there are firewall rules also created for all you know increased traffic there are firewall rules which are created and you can attach these firewall rules to a particular instance or you can have it for the whole subnetworks let's go back so that's the default vpc that is created by default for you by google platform per project the second one is auto mode vpc and this is you can create a vpc here if i go ahead and say create vpc network you can either say custom vpc or auto mode vpc if i say auto mode vpc then that this is what it changes so you will have subnetworks routes created by default for you and you do not need to worry about it when you click on auto that's where you need to add the subnetwork wherever you want to have it as an example right this one so i'll go back so auto mode vpc has got a single subnet per region by default created it has got fixed slash 20 uh side range per subnet you can expand that up to slash 16 uh default network is auto mode network and predefined ip ranges are there so you you are not creating any ip ranges so if you look at this one automatic you have these subnet ranges already created ip ranges already created and that is by default firewall rules already created for you okay and the routes also created for you you do not need to create anything in case of auto mode vpc if at all you want to disable something you can just go ahead and disable that custom mode vpc uh no default subnets are created if i just go ahead and uh make this as a custom vpc let me just create it custom vpc and let me see custom and i can just go ahead without any uh configuration i can make dynamic routing rules whether it is regional or global and we are going to get in details here uh but i'm just leaving it as is now and you can have dns policy also you can just go ahead and create it so the custom mode vpc is getting created so it has got no manual uh no default subnetworks uh manually created subnetworks can be valid uh any valid rfc 1918 ip range uh range do not have to be contiguous between the subnetworks because you are defining your own uh subnetwork ranges right and you have full control of ip ranges going back here if you look at this custom mode vpc does not have any subnetworks and it is custom right no firewall rules created no uh dynamic routing i can just go ahead and add subnetwork subnetwork us west 01 and i can go here us west 1 i can actually define ip ranges and you can go up to 16 per uh per ip ranges or cider ranges you can have google private access on or off you can enable flow logs and flow logs is very important when you want to audit uh who is accessing what data and you want to monitor the network and that's where flow log will be useful we'll we'll go ahead and get into it but i'll just go ahead and add this now so it is creating my uh subnetwork inside custom vpc there's not yet created i think okay yeah it's ready now so i have subnetworks created but if you look at firewall rules none of that is created so if you look at this network for this network uh we have firewall rules but no firewalls rules created by default for custom network the routes if you look at the routes there are two routes created one is default gateway to the internet and one for your uh subnetwork that is created so routes are created for the subnetwork which you have it but not for the other subnetworks which you have not created or any other region or zones right if you want to connect to so that's the custom vpc so in vpc as a summary if you look at you can create per project you can create five networks and that is by default which is the quota which you get it from google cloud platform so you can create say prod dev stage and all that network or based on the department also you can create the network and you can create different subnetwork to isolate different kind of environment like dev stage and prod right uh those networks are global in nature it is not tied to one particular region uh and you can create any resources in any region within those uh network uh it can talk to each other with the internal network communication so it it does not have to use uh external IP address to connect to each other or talk to each other but if you have if you are creating a resources in different network all together this communication is the external uh traffic and the internet egress uh charges will be applied for this particular communication just i need to keep this in mind so if at all you are uh creating resources in different region within the network then it is internal google internal network and there is there are no charges incurred as a internal to the network uh there are some you know cross region charges which is minimum but uh it is it is treated as an internal to the network but if you are uh connecting two resources in different network all together even though they are in the same region it is treated as uh internet egress traffic cloud networking hybrid connectivity we are going to get into details of the connection between your own premises uh to your vpc and we already saw vpc how you can create your private networking in public cloud environment now we are going to see how you can connect that private environment of private network with your own data center if at all you have it or your office so hybrid connectivity or the connection from your own office to your cloud or your private network is a part of networking service and networking service is a part of one of the three core services that are available in public cloud environment we are going to see we already saw vpc that's your private uh you know networking in cloud we are going to connect you are now the vpc with your own uh premises using vpn or interconnections and we are going to get into details of that right now so to connect your own premises with uh with gcp you have multiple options and you can use based on what is the use case that you have it so the first option and this is just copy paste you can think of from google cloud platform the first option is google cloud interconnect and what is that it is so this is required wherein you want to have considerable amount of you know data exchange between your google cloud platform and your own office not your end customers but your own office or your own data center and that's where you use uh cloud interconnect we are going to get into details of interconnect shortly uh but wanted to just give you high-level overview the other option that you have if you do not want to have dedicated connection is cloud vpn and this is you can think of it's like traditional vpn or ipsec vpn which you can use it over public internet there is no physical connection exists when you are using cloud vpn it is just going via public internet and why you use it you have you do not have actually the use case to uh connect your cloud interconnect or to have a pipe or dedicated pipe from the gcp vpc to your own premises and that's where you will start with the cloud vpn it is low cost as compared to interconnect and it has got its own benefit and disadvantage as well but that's the second service the third one is the peering and this is not purely a part of google cloud platform but you can think of peering is required when you want to connect your google cloud google platform like g-suite applications you want to access all other google services and you want to you know take advantage of reducing ingress fee and that's where you use peering you have multiple options they are in peering as a direct peering and carrier peering and we are going to see the differences in all of those shortly so the first one is cloud interconnect so cloud interconnect let you connect your own premise or the data center you can think of with google cloud platform the additional advantage which you get it out of cloud interconnect is you have dedicated connection to gcp directly and that's where uh you can exchange the data you can exchange the network configurations like if you have a sub network kind of implementation in your own data center and you your resources a virtual machine or physical servers wants to connect to the gcp resources or gcp wants to connect to your own servers that's where cloud interconnect will be able to help you to have dedicated internet connection or interconnections between your premises with with google cloud platform so high-level features it's a low latency highly available service that you can use it to connect your on-premise to google cloud platform you can have dedicated and partner connection options so you may have a location wherein you can get connected to google for it pop location but there are some companies they do not have interconnections or the location exactly near to the google pop location that's where you can go with the partner connection option it supports rfc 1918 what does this mean this means is you can have network exchanges between your on premises and cloud and all of these resources can talk to each other you can create ip space ip ranges that the way which we we have seen it in vpc right the similar way you can have the network connections or support between your on-premise or the office with your gcp vpc and it is a direct pipe or private pipe you can think of which is like data center from your data center to google cloud premises another option that we have is direct or partner pairing and why do you use it so this is not you can think of you want to exchange the network between on premises with google cloud cloud platform so everything is you can think of everything is there in your in the google and you want to just take down your egress fees and you want to have high speed connection and that's where you use direct peering you're not exchanging any network information with google cloud platform using peering option so the features you can have direct connections with google or partner wherever if you're not available there with the direction direct connection you can have vpn which you can configure or directly internet like public internet and you want to reduce the cost of egress fees right and that's where you use direct peering the third option that we have is cloud vpn and this vpn is you can think of traditional traditional vpn so why do you use it it has got a sla and sla of 99.9 service availability you can have site to site connections and you can create multiple connections to cloud environment or gcp environment from your own data center or offices it supports cloud router and we are going to get into details of what is that and what is that means to us as a cloud router but you can think of if you want to exchange the network information from your own premises with the gcp because that's how gcp service resources can discover your resources on premises the cloud router can be used and you can have encryption or secure traffic using cloud vpn and that's where you use cloud vpn so in total what we are going to look at is cloud interconnect cloud direct and carrier peering cloud vpn and cloud router cloud router is just an additional you can think of the service which you need to use it to announce the network changes if there are any and we are going to get into details this is high level you can think of decision tree and based on this decision tree you want to use you know one particular service or the other service to connect your on-premise data or on-premise office or your data center to google cloud platform if you look at very high level do you need direct access to your private computing resource on gcp the answer is no that's where you go to peering options right so this is where you are just accessing google cloud right from on premises and not your cloud resources accessing your on-premise data that is where it differs right do you need to connect your gsuite yes and can you meet peering requirement if at all you are there in google's location you can do direct peering if at all you are not there there are partners out there who can actually get you connected to google with their own connections and that's where you use carrier peering so this side that's where you want to exchange data between your on-premises and google cloud platform with the interconnections or the networking steps and that's where you take this route do you need to extend your data center to the cloud yes that's the requirement do you encrypt the sensitive information at the application level if you encrypt the sensitive information on the application level you can just go ahead and get into interconnect because interconnect does not provide you the encryption for that particular pipe if at all you need an encryption because your application does not do it you should go for cloud vpn okay so if you're if you do if your application is encrypting it or you do not need any encryption for your data you can take the interconnect route can you meet google's at one of our pop's location and that's where it says if at all you are there in the pop location that's where you can choose this route or you just go ahead and talk to interconnect partner and get it connected to google right is your need a 10 gb or more and that is where you have direct connections because if it is less than 10 gb google recommended to go with the partner because it's less it's cheaper right not going in too much detail here but the high level thought here if you need an sla that's where you will choose dedicated interconnect and vpn if you are least bothered about sla you will choose direct peering or carrier peering also more importantly if you want to exchange the network from your google from your own data center with google or you want to have direct connectivity between these two your own data center and google cloud platform that's where you will choose dedicated interconnect or cloud vpn if you do not need you know your on-premises resources talking to google cloud platform that's where you choose direct peering and carrier peering if you are just accessing g-suite applications or collaboration platform and you want to reduce the egress fee you are just going to go ahead and use direct peering or carrier peering based on where you are or where you are located okay that's in nutshell we are going to get into detail of cloud vpn in next lecture thank you where you can actually go and explore the hybrid connectivity options you can go to the console and you can go inside the network networking and you have hybrid connectivity and that's where you see three options right vpn and interconnects are the connection option and crowd router is just exposing or announcing your internet changes or network changes so if i go here you will not be able to see peering option here because peering is supported out of g-suite connection and not as a part of google cloud platform connection so you can go here and create a vpn connection if you want to have vpn connection you can go here and do interconnect connections if at all you have data center and you can replicate and you can have cloud router connected you know are created out of this one we are going to see in demo for vpn i will not be able to show the demo for interconnect because i do not have the data center to connect to but i can show you the demo from one region to another region on the google cloud platform itself and then we'll add cloud router as well to announce the network changes and we are going to see that in the demo as well so this is all about the hybrid connectivity guys if you have any questions on here you can wait for the theory for individual services or you can ask me in your in our questions thank you so in this particular demo we are going to see the bastion host how you can actually go ahead and set up the bastion host what i'm going to do is i'm going to use one of the virtual machine which is already there in the existing which i have created in existing lab and then i'm going to delete the external ip address for that particular server machine and then i'm going to create another virtual machine in the same subnet and i'm going to connect that so this is just i just wanted to show you that how you can actually delete the external ip and then how you can access it from outside so i have these virtual machine instances let me go ahead and delete the ip address external ip address for this one pvm one then go to edit i had to delete the network tag which was allowing all the traffic i have deleted the external ip address and then i'm going to send save it this is saved now you can see that for pvm one there is no uh there is no external ip address so you can't actually connect this one from outside now without actually creating any other machine right now i can just you know treat pvm two as a bastion host i can just go here i can just do ssh to the internal ip address it was so simple so uh you can actually do ssh to the hostname as well right pvm one see you are already in so this is how you can set up the bastion host i hope you understand this one this is very quick demo i just try to utilize whatever i had created in earlier our demo for this one as well this concludes actually the virtual networking configuration if you have any question on this one just let me know otherwise you can move to the next section in next section i'm going to cover the interconnect so if you have say my infrastructure or your own data center and you want to connect to a google cloud platform for additional services how you can actually do the interconnect uh that's what we are going to see it uh in addition to that vpn connection dns cloud dns and cloud cdn thank you stack driver using stack driver you can do cloud monitoring logging trace debunk and error reporting we already saw the core services like computing database storage and networking right but all of these services needs provisioning monitoring uh utter scaling load balancing right so all of that we need to have some infrastructure services who are managing those resources in the cloud right and that's where resource monitoring comes in a play as a stack driver offering uh stack driver monitoring does a platform or system application matrices you can do health check uh uptime check you can create multiple dashboards where you can monitor and you can configure even alerts in stack driver monitoring uh in stack driver logging you can have platform system or application log you can do log search view or filtration you can do log based matrices uh you can do debug in a production using stack driver debug you can take conditional snapshot and it has got id integration as well uh using stack driver trace you can do latency monitoring per url latency sampling and in error reporting you can do error notification and error dashboards so how stack driver is used uh stack driver is actually a multi-platform cloud monitoring service uh you can have gcp projects you can monitor gcp projects uh in terms of you know monitoring debugging error reporting logging and trace but you can also do uh that with uh aws projects right so you can do aws monitoring as well so stack driver works with gcp as well as aws stack driver needs to have host project to uh have an account and you can enable billing account uh around the host project uh this host project is in google cloud platform so you cannot have uh so if at all all the services which are there in aws you cannot have uh you know its account used in gcp you need to have uh a host project account uh to have this monitoring enabled so when we say stack driver a stack driver is very much integrated with all the services about uh out of gcp okay so what are what are the benefits having actually the pre-integration right so you can do monitor uh multiple clouds aws and gcp and those connectors are already available in stack driver you can identify trends and prevent issues you can reduce monitoring overhead you can improve uh signal to raise a nice ratio and this means is uh you need to have uh a good uh responses over the bad resources that's where you know uh you using stack driver you can actually improve that and you can fix the problem very quickly if you understand it very early let's go ahead and get into stack driver monitoring hello welcome to this section on big data service from google cloud platform big data platform services uspr unique selling point for google cloud platform and one of the oldest service from google big data platform solutions help customer to process huge amount of data without managing the underlying infrastructure for enterprises or customers it really matters how easily or efficiently they can ingest the data if it is stream or batch or existing data process the data or store the data do x data exploration using visualization or report the data google big data solution provides different solution to satisfy all of these requirement or similar requirements this is the naji i will take you through google's big data solution overview cloud certification does not cover in-depth aspect of big data solutions but what is what is necessary as it's one of the critical service from gcp so let's go ahead and get started big data solution as a pass platform as a service google offer big data solution as a platform service and different options on big data services are google big query which is uh edw or enterprise data warehouse uh in gcp google data flow uh or you can say stream processing uh google data prog which is hadoop and spark clusters uh google cloud data lab data exploration tool some of the other services which can be treated or they have a consult dated into big data service are google cloud pubsup and google genomics google big query is fully managed enterprise data warehouse for large-scale data analysis google big query it's fully managed enterprise data warehouse for large-scale data analytics petabyte scale and low-cost enterprise data warehouse for analytics big query it's serverless there is no infrastructure access to manage and you don't need a database administrator so you focus on your analyzing the data to find mini capable insight using familiar sql some of the product features of big query are flexible data injection so you load your data from google cloud storage or google data store or stream into big query at hundreds of thousands of row per second to enable the delta analytics into your data it's globally available so you have option to store your big query data in european locations while counting benefits from fully managed service now with option of geographic data control without low-level cluster maintenance or head act security and permissions you have full control over who has access to the data stored in google big query share data set is not impact your cost or performance cost controls big query provides cost control mechanism that enable you to cap your daily cost or to the amount that you choose highly available transfer and data replication in multiple geographic geographic means your data is available and durable even in case of extreme failure modes it's fully managed in addition to the sql query you can easily read write data in big query via cloud data flow spark and hadoop connect with google products you can automatically export your data from google analytics premium into big query visualize it using data studio and analyze data stored in google cloud storage automatic data transfer the big query data transfer service automatically transfer your data from partner SaaS applications to google big query on scheduled or managed basis this is all for big query so in that shell what you need to understand is big query is enterprise data warehouse google cloud data flow or Apache bin it is a stream or batch data processing so google cloud data flow offers a unified programming model and manage service for executing wide range of data processing patterns including stream analytics etl and batch computing cloud data flow frees you from operational tasks like capacity planning or resource management and performance optimization and like that some of the product feature of data flow it has got automatic resource management so cloud data flow automates provisioning and managing processing resources to optimize or minimize latency maximize utilization no more spanning of instances by hand or reserving them dynamic work rebalancing automated and optimized work partitioning dynamically rebalances logging work and no need to chase down hotkeys or pre-process your input data it's a reliable consistent and exactly once processing provides built-in support for fault tolerant execution that is consistent and correct regardless of data size cluster size or processing pattern or pipeline complexity it's horizontally auto scaling of worker resources for optimum throughput result in better and overall price to performance ratio it supports unified programming model apache bin SDK offers equally rich map reduced like operations powerful data windowing and fine green control or correctness control for streaming and batch data like it's a community-driven innovation so developer wishing to extend the cloud data flow programming model and can fork and or contribute to the apache bin so you can actually think of this data processing logic google has open sourced it in the name of apache bin in nutshell what you can actually see is data processing can take a data from stream or a batch of any kind and we'll talk about what is purpose of and all of that but it can take batch job or stream job it can process into data processing logic which is in data flow and then it can pass the information to either the BigQuery cloud machine learning or big table from which you can draw your either machine learning analytics or you can see the insights from the data cloud data proc it's a faster easier more cost effective way to run your spark and Hadoop so you can actually think of data proc is your Hadoop cluster or spark cluster so use cloud data proc as a managed spark or Hadoop service to easily process big data set using the powerful open tools in apache big data ecosystem a cloud data proc integrates with storage compute and monitoring services across the cloud platform products giving you a powerful and complete data processing platform some of the features of data proc it's automatic cluster management so managing or deploying logging monitoring let you focus on your data and not on your cluster so your cluster is stable scalable and speedy you don't have to manage the the cluster a resizable cluster so cluster can be created and scaled quickly with variety of virtual machine types disk size number of nodes networking options it is integrated with other gcp services and you can actually plug this with cloud storage big query big table stack driver logging stack driver monitoring which gives you a complete and robust data platform image versioning image versioning allow you to switch between different versions of apache spark or apache Hadoop and other tools it's highly available you can run your cluster with multiple master nodes and set the job to restart on failure to ensure that your cluster and jobs are highly available developers tools multiple ways to manage your cluster including an easy to use web UI google cloud SDK restfully apis and ssh access initialization actions you can actually run initialization actions to install or customize settings and the libraries which you need when you when your cluster is created automatic or manual configuration cloud data proc automatically configures hardware and software on your cluster for you while you are allowing for while also allowing you to manually control the configuration you have flexible virtual machine which you can use so cluster can use custom virtual machine type or prem table virtual machines so that you have perfect size for your need cloud data lab cloud data lab is an interactive notebook based to explore collaborate analyze virtualize the data it is integrated with BigQuery and cloud machine learning to give you easy access to your key data processing service so this is an supplementary service for your data analysis or exploration some of the product features like other big data solutions this is also integrated with other gcp services the cloud data lab simplifies the data processing with your cloud BigQuery cloud machine learning cloud storage and stack driver monitoring authentication cloud computing computation and source controls are taken care out of the box you don't have to worry about it has got a multi language support cloud data lab currently supports python sql and javascript for BigQuery as a user defined functions notebook format cloud data lab combines the code documentation results and visualizations together in an intuitive notebook format it's a paper use pricing so only pay for the cloud resources when you use interactive data visualization use the google charting for easy visualization machine learning so supports tensorflow based deep machine learning models scale training and predictive via specialized libraries for the cloud machine learning engine ipython support data lab is based on jupiter five formally ipython so you can use a large number of existing packages for statistics machine learning etc and it's open source cloud pub sub cloud pub sub is ingest event streams from anywhere at any scale reliable real-time streams analytics pub sub is serverless large-scale and reliable real-time message processing service that allows you to send and receive messages between independent applications you can leverage the cloud pub subs flexibility to decouple the system and components posted on cloud platform or elsewhere on the internet by building and building on the same technology google uses cloud pub sub is designed to provide at least once delivery at low latency with on-demand scaling to tens of millions of messages per second so some of the product features at least once delivery it synchronizes cross zone message replications per message receive tracking ensures that at least once delivery at any scale it's exactly once processing with cloud data proc supports reliable expressive exactly once processing of cloud pub sub streams no provisioning so cloud pub sub does not have shards or partitions just set your quota publish and consume it integrates with other gcp services open apis and the client libraries even libraries in seven languages support cross cloud and cross hybrid deployments global by default you can publish from anywhere in the world and consume from anywhere with consistent latency and no replication necessary compliance and security cloud pub sub is HIPAA compliant service offering fine-grained access control and end-to-end encryption you can actually visualize in this particular picture as cloud pub sub is an ingestion engine from any application devices or any data set and that can actually pass the information or ingest the data into the cloud data flow likewise there are many more use cases for cloud pub sub probably i can create another video to detail out pub sub because pub sub is much more important service not even for big data but other application in gcp domain that's it for this particular lecture so you can actually think of big data is managed service you don't have to provision the resources in majority of the cases and you don't have to provision the resources you just need to use it and based on how much you use it you will be charged so it is pay as you go amount of data whether it is amount of data or volume of data which is ingested or data is processed by those applications cloud architect on the big data will ask about overview questions only you need to have understanding on what is what to pass the exam i will create a detailed course on big data with the demo for your understanding only or if you're planning to give data engineer certification it will be useful that's it for big data as a overview section if you have any questions let me know otherwise you can move to the next section thank you hello welcome to the section on artificial intelligence or machine learning service from google cloud platform artificial intelligence platform service is another usb besides big data for google cloud platform and its oldest success full service where they don't have almost any competition ai or machine learning platform solutions help customer to use ai or humanization of their own software data without managing underlying software or infrastructure enterprises or customers it's really matter how easily or efficiently they can use machine learning apis speech recognition image analysis video analysis text text analysis like sentiment analysis or language translation which we know translator google.com machine learning along with other services provide solutions to satisfy all of these or similar requirements this is naji i will take you through google's machine learning solution overview google certification cloud certification does not cover in-depth aspect of machine learning solutions but what is what is necessary like big data as it's one of the critical service from gcp so if you're ready let's go ahead and get started ai or machine learning solutions is platform or software as a service google offers artificial intelligence or machine learning solutions as a platform service and different services under ai or gcpr cloud machine learning which is tensor cloud video intelligence api vision api cloud speech recognition api cloud natural language api cloud translation api and cloud job discovery so let's understand what is cloud machine learning so it is fully managed machine learning on any data any size it's a tensor flow implementation of machine learning so google cloud machine learning engine makes it easy for you to build sophisticated large-scale machine learning models that covers a broad set of scenarios from building sophisticated regression models to image satisfaction to image classification some of the product features it is integrated with other google services and designed to work with data flow for feature processing cloud storage for data storage and cloud data lab for model creation you can discover and share samples so machine learning is tailored to sample your data for industry use it's hyperduo it's built better for performing models faster by automatically tuning your hyperparameters with hypertune instead of spending many hours to manually discover values that will work for your model so it is managed service which focus for you to focus on model development and production without worrying about the infrastructure so managed service automates all the resource provisioning and monitoring scalable build your model for any data and any size any type managed distributed training infrastructure that supports cpu and gpu accelerate your model development by training across many number of nodes and running multiple experiments in parallel notebook and developer experience you can create and analyze models using the familiar ipython notebook development experience with integration to cloud data lab portable models you can use open source tensor flow sdk to train your models locally on the sample data and use google cloud platform for training at a scale the models are trained using google machine learning engine and can be downloaded for local execution or mobile integration so this is all about the cloud machine learning so you can actually think of this tensor flow implementation with additional sdk or apis for you for ease of use cloud video intelligence api you can search discover media content which is video here a cloud video intelligence api makes video searchable and discoverable by extracting metadata with easy to use rest api you can now search every moment of every video file in your catalog and find every occurrence as well as its significance it is quickly annotate videos stored in cloud storage and it helps you identify key nouns nouns entities of your video and when they occur within the video some of the product features it will give you insight from your videos so apis allow you to extract actionable insight from the videos files without requiring any machine learning or any computer vision knowledge cloud video intelligence api improves over a time and new concepts are introduced and accuracy is improved you can have label detection so it detects entities within the video such as dog flower cat and like that short change detection it can detect the changes in scenes recognitions it can specify a region where the processing will take uh processing will takes place integrated with other jcp platform it can access via rest api or client libraries over seven languages to request one or more annotations type per video and we'll see how that actually happens in small demo and you don't have to manage any infrastructure for this one as an example here i just took it from the website itself you can actually go ahead and give the video to the api and it will give you video labels it will give you shots short changes and short labels and then it will give you rest response and you can use that response in your programs as an example here let me pull the demo video so when you go to actually uh products machine learning and video intelligence api you can either select sample api sample video there any of this one right this is going to take time because ultimately uh it has to complete the analyzing the video you can go ahead and try uh this on website on your own and you'll get uh the outcome like this one cloud vision apis google cloud vision api enables developers to understand the content of image by encapsulating powerful machine learning models in easy to use rest api it quickly classifies your image into thousands of categories which is already defined and detects individual objects and faces within the images and finds reads printed words contained within the image you can actually build the metadata of your image catalog moderate offensive content or enable new marketing scenarios through images sentiment analysis it analyze images uploaded in the request or integrates with your image storage on the cloud storage so some of the product features of image apis uh it's labeled detection you can detect broad set of categories within the images ranging from models to transportation to animal uh it can detect explicit content uh it can detect levels landmark detection is also possible with uh vision api optical character recognition it can detect an extract text within the image uh with support for broad range of languages and along with support for automatic language identification you can have face detection so detect multiple faces within the image along with associated key facial attributes like emotion state or wearing headwear image attributes it detects general attribute of image such as dominant color and appropriate crop hints it can detect web web detection search the internet for similar images it is integrated with a rest api that's the vision api actually I have passed one of the image from Seattle and then it has given me some of the information here so label it says it is city skyline metropolitan area skyscape metropolis urban area daytime at the same time it can actually give you the properties image properties and whether the content is adult spool or medical or violence if I go to the website for image let me let me bring one of the image and let us try to understand more of this particular image so it has given you the information like mixed use urban design neighborhood 81 chances it's a real estate 69 percent charges pandominium 68 percent city 67 percent shopping mall 63 percent plaza trees recreation right uh if I have to go and click web you can actually very well see that it's a Microsoft visitor center and there are chances more chances uh Microsoft Redmond campus building 111 to be very precise Microsoft corporation the text information which you have here is eel I don't know where it is resides the color properties you have it based on these particular chops safe search adult it's very unlikely very unlikely medical very unlikely violence very unlikely and this is the outcome in your json you can actually we will see that it's urban design neighborhood and likewise you have multiple information whether it is safe search very unlikely very unlikely so you will get that actually from the api as an outcome that's the vision api google speech api so speech or to take a text conversion so google cloud speech api enable you to convert your audio file into a text by applying on neural network models in easy to use api the api recognize or 100 plus languages and variants to support global user base you can translate the text of the user detecting to an application microphone or enable command line or command and control through the voice among many other use cases google speech api so it is speech to text conversion google cloud speech api enable you to convert audio into the text by applying neural network model in an easy use api the api recognize or 100 plus languages and variants to support global user base you can trans transcribe the text of users detecting to an application microphone or enable command line control through voice among many other use cases some of the product features it is powered by machine learning the return results are in real time it is context aware so speech recognition can be even tailored for your context by providing separate set of words hints to each api call which is useful especially for device and up control use it works with app across any device so this speech api supports any device that can send rest call or g rpc request including phone pcs tablet iot devices and whatnot automatic speech recognition so automatic speech recognition asr powered by deep learning neural networking power your applications like voice search or speech transcription it has got a global vocabulary it recognize 110 languages and its variants with extensive vocabulary streaming recognition it returns a recognition recognition results while the user is still picking the word hints speech recognition can be customized to a specific context by providing set of words and phrases that are likely to be spoken especially useful for adding custom words and names to the vocabulary and even voice control to use case it's a real time and prerecorded audio support as well noise robustness it can handle noise audio from any environment without requiring additional noise cancellation inappropriate content filtering it filters inappropriate content in text results for some languages integrated api audio file can be uploaded in the request or integrated within google cloud storage some of the example here which i tried to do it earlier you can convert your so i was speaking it and then it was converting in real time i selected another language here and then that has also converted into it let me go ahead and show you how actually does it work hello it's me we are learning google cloud platform one of the other language let me select which i know google and this is how it works in real time so that was speech api cloud natural language api you can actually drive insights from your unstructured text so google cloud natural language api reveals the structure and meaning of text by offering powerful machine learning model in easy to use rest api you can use it to extract information about the people places events and much more mentioning the text document news articles or blogs some of the features you can have a content classification relationship grab so you can classify your document by common entities around 700 plus general categories such as news technology entertainment you can build relationship grab of entities extracted from the news wikipedia article or by using signal from the state of art syntax analysis syntax analysis you can extract tokens and sentences to identify parts of speech pos and create dependency phrase freeze each sentence entity recognition identify entities and label by types such as person organization location events products or media you can have sentiment analysis you can understand overall sentiments expression in the block of text the content classification you can classify document pre defined 700 plus categories sentiment analysis you can understand overall sentiment expression in the block of text content classification you can classify your document in pre defined 700 plus categories multi language enables you to easily analyze text in multiple language including english spanish japanese chinese french german italian korean and portuguese integrated arrest api access via rest it can be uploaded in a request and or integrated with you can see you can store that in cloud storage so one of the example here which i have up forward is and the output which i got it is you can see the first one the google is an organization that's what it has detected the sentiment the second one is the users third one is the phone android the person name mountain view is the location uh consumer electronics an event consumer goods which is phones and keynote let us analyze the content here in here so it has detected location native americans war history war an event again north america george washington people colonies shelters leaders location colonies other colonies yeah so these are the information which you will get it after analyzing this particular text which i copied it from this one and that's our natural language apis will help you to understand more about your the text google cloud translation api it is fast dynamic language translation the google cloud translation api provides simple programmatic interface for translating an arbitrary string into any supported language our translation api is highly responsive so websites and applications can integrate with the translation api for fast dynamic translation of your source text from the source language to the target language some of the product features of translation apis you can translate mini language so somewhere around 100 plus language is supported currently you can detect the language it has got a programmatic access so with apis you can restful apis you can use that in your programs you can do text translation it's continuous updates so behind the scene the translation api is learning from the logs analysis human translation examples existing language pair to improve and new language pair comes in online with no additional cost translation api has got a adjustable quota you can increase from 2 million per character to 50 million per character and even that can be changed it is affordable and easy pricing some of the example here i don't have to actually give you a complete example here because many people and used translate translator google.com it's a similar context it is just rest interface to that that's it for AI or machine learning cloud architect exam particularly ask only the overview part of this particular section and that's it if you have any questions on machine learning or AI please let me know otherwise you can move to the next section