Dear students, let me present to you the concept of transformation in the case of a bivariate probability mass function using the MGF technique. And I will do it with the help of an example, let X1 and X2 be discrete random variables and let them have the joint PDF p of X1, X2 equal to mu 1 raised to X1 into mu 2 raised to X2 into e raised to minus mu 1 into e raised to minus mu 2 over X1 factorial into X2 factorial where X1 takes on the values 0, 1, 2, 3 and so on and X2 also takes on the same values and the PMF is equal to 0 elsewhere mu 1 and mu 2 being fixed positive real numbers. Now given this scenario, suppose that we define Y as X1 plus X2 and we are interested in determining the PMF of Y. Obviously, if we are dealing with a discrete variable situation, if X1 and X2 are discrete random variables then Y also will be a discrete random variable. Before we go to the MGF of Y, let me talk about the MGF of X1 and the MGF of X2. It is the product of two PMFs which are the PMFs of the Poisson distributions having mu 1 and mu 2 as their parameters respectively. Poisson distribution, would you not agree that for the random variable X1, we will say that p of X1 is equal to e raised to minus mu 1 into mu 1 raised to X1 over X1 factorial where X1 is equal to 0, 1, 2, 3 and so on and similarly for the random variable X2, if it is Poisson with parameter mu 2, its PMF is given by e raised to minus mu 2 into mu 2 raised to X2 over X2 factorial where X2 is equal to 0, 1, 2, 3 and so on. So if you multiply these two and construct this bivariate PMF, that means that we are dealing with that distribution, that bivariate distribution which has come about in that situation where we have two independent Poisson random variables. So by multiplying them, we get the joint PMF. By the basic definition of the MGF, the MGF of X1 is the expected value of e raised to TX1 and after solving and doing the algebra, we obtain e raised to mu 1 into e raised to T minus 1. In any power kender, we have mu 1 into e raised to T minus 1. Similarly for the MGF of the other Poisson random variable X2, we will have exactly the same kind of an expression e raised to mu 2 into e raised to T minus 1. Now let us try to find the MGF of Y. We have defined the new variable that Y is equal to X1 plus X2. If we want to remove this MGF, students, what will be the formula? It will be the expected value of e raised to TY and how do you define expected value in the case of a bivariate PMF? Well it is the double summation, summation over X1 and summation over X2 of e raised to T into X1 plus X2 because Y is equal to X1 plus X2. So e raised to T into X1 plus X2 and this whole thing multiplied by P of X1, X2. You need double summation to have this product over all possible values of X1 and X2. But students from basic algebra, we know that we can take some of the things out of the second summation sign. If the first summation sign is with respect to X1 and the second summation sign is with respect to X2, then all the terms of X1 will come out. We have the expression inside e raised to T into X1 plus X2. You can write e raised to TX1 plus TX2 and you can write e raised to TX1 into e raised to TX2. This is the E portion with which P of X1, X2 is written. As you know that is mu1 raised to X1 into mu2 raised to X2, e raised to minus mu1, e raised to minus mu2, this whole thing over X1 factorial, X2 factorial. They will come out of the second summation which is over X2. So what will be the expression then? The expression will be expected value of e raised to TX1, mu raised to X1, e raised to minus mu1 over X1 factorial. After this, we will be writing summation over X2 of e raised to TX2, mu raised to X2, e raised to minus mu2 over X2 factorial. Now, let's further solve this and you will find as you can now see on the screen that after doing a few steps, you obtain the product e raised to mu1 into e raised to T minus 1. This thing multiplied by e raised to mu2 into e raised to T minus 1. If we solve this product further, you will know that power will be added and e raised to T minus 1 common will come out and we are left with e raised to mu1 plus mu2 and this whole thing multiplied by e raised to T minus 1. But students, the product form. Now, a little while ago, I had discussed MTF of X1 and MTF of X2. Are not these two exactly the same? Yes, of course, e raised to mu1 into e raised to T minus 1 is the MTF of X1. Similarly, the other one is the MTF of X2. So the point is that here you have to see that after solving it, the final expression is that its form is not exactly the same form which is different from the first one. In power, mu1 is written in the beginning and that is the MTF of X1. In the second one, mu2 is written in power and that is the MTF of X2. Now, the final answer is that in power, mu1 is written in the beginning and mu2 is written as mu1 plus mu2. But the rest of its overall form is exactly the same as that of those two. So this means that the random variable Y also has a Poisson distribution. The only thing that the parameter of this particular Poisson distribution is neither mu1 nor mu2 but mu1 plus mu2. Then look again at the MTF, which is the uniqueness property of the MTF. The form of the MTF is recognized by that distribution. So the form of the MTF is exactly that of the Poisson distribution and therefore, students, we can write the PMF of Y as follows. P of the random variable Y at the point small y is equal to e raised to minus mu1 plus mu2 equal to mu1 plus mu2 whole raised to y over y factorial. Now, we will write y and of course, after this we have to put a comma and after that we have to write the values of y and students y will be equal to 0, 1, 2 and so on. Exactly the same for the Poisson distribution. If you want to know how come y is equal to 0, 1, 2 and so on, then go back to the point where y was defined. Y was equal to y is equal to x1 plus x2 or x1 key all possible values 0, 1, 2, 3, x and so on. x2 key all possible values 0, 1, 2, 3 and so on. Now, you start adding. What might be the minimum possible sum? 0 plus 0 is equal to 0. After that, additions. 0 plus 1 is 1, 1 plus 0 is also 1 and 0 plus 2 is 2, 1 plus 1 is 2 and 2 plus 0 is also 2. So, you see and you will have no doubt that y also assumes the values 0, 1, 2 and so on. So, this is a very, very interesting way of finding the PMF of a transformed variable y based on original variables x1 and x2. Whose MGFs are available are known and if later we find that the MGF of this y is also of the same form, we conclude that this new variable also has the same distribution.


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Hi, I'm Dr. Yogesh Kamat, I'm a specialist hip and knee surgeon and I'd like to talk to you a little bit about cartilage regeneration. Now, who hasn't heard of arthritis? It is something which is there in every family, either a friend or a relative. Now, it is common that senior citizens get arthritis and many, many people are going towards joint replacement surgery. However, when the average 40 or 50 year old working person gets rid of joint pain, does that mean that they are proceeding towards joint replacement surgery? There is a whole range of symptoms and structural problems under the wide group of arthritis. It is all about picking arthritic changes relatively early. Particularly in young people, when I say young, I'm talking about everybody who's not a senior citizen. Particularly in these, if they've had pains after some injury, there could be lesions which are not really detectable in x-rays and lesions in the surface of the joint that is the cartilage surface which are amenable to treatment by which joint cartilage can be regenerated. Now, there are various techniques and these have been developed in the last few years. They are not alternatives to joint replacement but options for people who have early damage in their joints. Now, this needs to be looked at rather than being lived with. You could feel free to consult a specialist in these procedures and there are a few in India where we can get specific MRIs which tells us about the conditions of cartilage in the joint. There could be a lot of structures in the joint which can get damaged and can be made to regenerate by specific techniques. Some of you might have heard about stem cells. Now, it's not just stem cells but we can use our bone marrow. We can use specific techniques where cartilage can be taken from areas where it is not essential and be made to regenerate outside the body and re-implanted back in the right conditions. These need to be seen by a specialist and can definitely work towards making your joint normal.


Who and what is Independent Sage? Independent Sage is a group that describes itself as a group of scientists who are working together to provide independent scientific advice to the UK government and public on how to minimise deaths and support Britain's recovery from the COVID-19 crisis. Who is in this group? Well, that's very interesting. Some reports over recent months have revealed that the group is packed with left-wing activists. An NHS doctor, for example, has expressed concern that there is, quote, something quite disingenuous about a supposedly independent scientific group being formed by what appears to be a political organisation. Unsurprisingly, the National Press has given this group an enormous platform. For a two-week period, this group appeared on BBC Newsnight, Sky News, BBC Politics Live, ITV News and In The Guardian. Independent Sage is at the forefront of criticising any measures by the government to ease restrictions and the press gives it a platform to do so. Once again, this is unsurprising. The press across the board is presenting the easing of restrictions in a negative light and has been hyping up the coronavirus crisis from the get-go. The media, as we know, does tend to sympathise with left-wing views and groups while disallowing any dissent. The easing of coronavirus restrictions is no different. One incredible example of press bias was the BBC's Panorama. It was entitled The Disinformation War. In other words, right there in the title, you are told that there is only one side to this debate, the pro-vaccine side. Anti-vaccine is disinformation. In this documentary, Dr Vernon Coleman was castigated as a misinformer. He was one of many. But what's interesting is that while the BBC spoke about Dr Coleman, they didn't speak to him. His views were matter-of-factly dismissed. There's no debate, no dissenting view. Disagreement with the BBC narrative is the stuff of cranks. No evidence for either side was put forward. This unfortunately is modern-day journalism. One side of the narrative put forward, those who disagree are dismissed and all as a matter of fact. While the press will talk about people smearing and slandering them, they don't speak to those people so there's no chance for them to answer back. As Independent Sage demonstrates, the left-wing is keen on coronavirus restrictions. They are keen on these restrictions for political reasons, not scientific ones. Massive state control of our everyday lives. It's no wonder the left admires it so much. That's the left control. And what the left-wing wants, the press wants. The media is always happy to present left-wing views as the only views worth listening to. It goes without saying, this is not democracy.  We insist that all sides of a debate are presented in the media and people therefore able to make up their own minds about any given issue. So join us, insist on debate, insist on truth, insist on the right to dissent.


All right, it's pretty amazing that we have so many Red Haters here, so Take advantage, right? This is my favorite part of OpenShift Commons is the AMA. So we're going to do a quick round of intros and Then we'll get your questions answered, right? So I'm Karina Angel. I am one of the OpenShift product managers and My focus area is cloud packs if you've heard of our partner IBM, right? We have a collection of software called cloud packs and I focus on that as well as upstream projects My name is Daniel. Oh, I'm developer out of Kate Java and OpenShift a lot of stuff so far as service and match thing. Yeah Daniel you didn't even say the word corkis. Oh Come on. Yeah, I'm at the Kubernetes Java stack corkis I'm one of the main responsibility And I'm also the CNC for investors. Just let me know anything about CNCF project I'm Daniel Messer. I'm a product manager at OpenShift for Red Hat Quay, Quay.io and the operator life-cycle manager Ramon Acedo Rodriguez, product manager for OpenShift. My focus area is OpenShift on bare metal I'm Jan Schafranek. I'm not a product manager I'm a software engineer for storage Kirsten Newcomer OpenShift product management focused on cube and container security Ramon Roman here yet another product manager for the migration toolkit for applications and very involved in the conveyor community Connor Gorman. I'm an engineer on the ACS and Stack Rocks team Perfect All right So I know this is the hard part. It's the end of the day. Does anyone have a question? Or are we gonna be making softball questions for these folks? There's a question here and You've got the the other mic. It's got the mic So right over here So how does this OpenShift and OKD really relate? Who's upstream? Who's downstream or is it just a mesh? Christian, where are you? I think Christian and Vadim we sent them off to go talk to one of the other upstream projects However, I have been working in the OKD world for I think I have a microphone I'm the one person who's still mic'd up. I like to call OKD a sibling stream Okay, so it is built on the same release process as by the OpenShift CI CD build process so it is Kubernetes is really our upstream or for OpenShift and OKD and all of the other Things that we build with OpenShift But it so I would call it a sibling stream and we have a really great relationship with the Fedora core OS Team and we do most of the testing and deployment within the OKD working group On the different variations of where people like to deploy it So if you go to Google groups, there is an OKD working group If you go to the Kubernetes slack channel and to OpenShift dash users You can find the OKD working group and get involved. So good question. He gets the t-shirt. All right Any other questions here? There's one over here. I'm making Michael the tallest man in the room Hello, thanks for all the presentation. Very interesting today I have some question about migration toolkit conveyor and also security about six store That's all these product Are supported in this connected mode and are there any requirements or for that Migration toolkit for applications you mean and the whole conveyor suite or yeah, but even application and containers Yeah, I can go I can go for my migration. So In the conveyor project, I mean it depends where where your Origin is what what is your source for migrating applications into into Kubernetes? I don't know if you will be interested in application coming from Let's say legacy classic Runtime platforms or what what kind of a migration are we talking about? Yeah, more about legacy application okay, so for for applications it doesn't matter that much the the Deployment model that you might have for for Kubernetes We focus on assessing the application portfolio that would be a questionnaire-driven assessment and then on the analysis we will be working mostly with a Source code and and binary so we wouldn't be that interested on the on the platform Sorry platform itself then for example with move to cube you would be able to Based on the application type that that you are migrating Let's say you have a Java application running on Tomcat move to cube is able to understand What kind of application you are based on the on the dependencies and generate the deployment manifest So not so much about the kind of deployment model you might have I don't know if that answer your question Yes, but it was also about When we have a open shift cluster in this connected mode air gap mode That's the all the thing that I think there is an analysis tool with AI and some of the IBM Modules in the in the migration toolkit does they need to access internet or services or Okay, sorry That shouldn't be a problem because the knowledge base is building in the process and project itself so We wouldn't need a Direct connection to the internet everything is pretty self-contained or can work in self-contained Mode right now. I'm not sure about the Design, I know the current design for the AI Thingy powering the IBM research tooling is self-contained But I do know that they were thinking about having some central knowledge base or something like that But I don't have the details about how this thing will evolve in the future But for the moment as it is right now it's self-contained Thank you welcome So quite the same question for a six star. Yeah, so So you're asking specifically what what can I use with six store in a disconnected mode, right? so as Luke said earlier today six store has kind of multiple components, so Creating, you know using a open shift pipeline and connecting the ACS scanner connecting with quay If or what other whatever registry you have All of that can be done in a disconnected environment Tecton chains can run in a disconnected environment the ACS image Admission controller to validate the signature all of that can be disconnected a piece of the puzzle that right now We have not yet shipped an instance of recor Which is the signature transparency log? So if you need to validate that signature if you if you need to check, you know audit the transparency log Right now. That's not something that would be available on premises. It is in our plans to Make it, you know to ship something that then you could deploy yourself to ship a recor operator You could deploy an instance of recor yourself But right now the recor I think there's an instance available from Linux Foundation, but that is an online access so so eventually Okay, thank you sure thing All right. There's another question Hi Sorry if we wanted to start with a like a we've we've begun our cloud journey and we've built some Arrow clusters and We've got our dev and our prod and we want to start creating our dr instance, which we've already deployed But how do we begin that journey of actually creating that replication in between? Let's say our prod cluster and what we want to set up as our dr cluster in case You know what a disaster actually hits and we need to start I'm saying I'm pretty sure there's some documentation on it But if you were to just give some general advice of where you begin that journey, that would be helpful Who's the disaster recovery person? The question is about disaster recovery and strategies strategies with disaster recovery with open shift Okay, so this is a difficult question because open shift is designed for Applications that you can distribute right so most of the time what you do is you have the disaster recovery layer above open shift at the application layer and then You will take care of your application being properly distributed having said that obviously open shift has in every cluster mechanisms for You know if one note goes down Then there's an evacuation Another note I can take over the workloads that happens. That's by default that you have it I'm not sure if your question is more about I have it's it's more like if your your clusters are in different regions All together you want to make sure that in case That's cloud exclusive, but it's not too exclusive Now it depends hot hot hot cold and that's where you start from now with the cluster itself it depends on Let's start by the simplest one hot cold, right your cluster will You have a definition for your cluster for instance in the cloud You can have an arm template to deploy your algebra to open shift cluster or Terraform or an ansible book whatever So that's where you start with so you're gonna have that ansible book That is deployed in a different repository in a different region, right that you can access in case of disaster disaster So that's the coldest possible thing that you can do You can have the cluster deployed as well in the different region. That's a bit hot, right? Then we're gonna move to the applications now on the application ends at the question is do you have a stateful or a Stateless cluster and a stateless cluster. That's the easy possible thing, right? So I have my pipeline it can spit the applications into two clusters, right? So I have this my front-end application that will be deployed in region one and this and at same time We can deploy it in region two then I need to have a global low balancer that can point to two clusters assuming I need to you know have an active active setup for instance, right now when it comes to the stateful workloads This is where things get interesting don't do stateful workloads unless you need them right inside the cluster So in the cloud what we advocate customers to do is hey Go and use a managed service because that managed service has the underlying Backup and disaster recovery and replication underlying in it, right? So you just point to a my sequel server or my sequel as your my sequel service that does Logical replication to a different region and then your data will be there And then your front-end application that live in an openshift cluster You just need to deploy them in the other end now if you have a stateful cluster for instance, you have my sequel running in the cluster The folks at cockroach they figured it out They have logical replication the application level But that's not the case for the rest of the things like my sequel or postgres or MongoDB and so on there You're gonna have to have a backup and restore solution for instance, right? One of the most popular ones in the community is Valero, right? So you can snapshot that underlying data and then restore it in some other region and You know keep restoring it in some other region and you know point the other cluster to that database, right? so that's that's how you think of it and It's a matter of cost of complexity You can go for active active and that's gonna be the most expensive one You can go for active cold and that's the expensive one, but it depends now RTO and RPO discussion, right? Hopefully that answered the question. All right. Thank you Thanks, I think this was a great answer and I think you heard a theme, right? So it depends on what you need to restore I did he would start by applying GitOps from the very first day that will maintain your desired state And configuration all in git which you can just redeploy in your dr. Cluster There is this question about data restoration data movement, right? so on the road map this morning you may have already heard this but redhead ACM will have a orchestrated failover process this year that relies on background as in chrono's data replication and Movement of the application differences themselves to a dr. Cluster, so this is a going to be a first-class concept in ACM Which will rely on ACM's native capability to apply and manage application Manifests across clusters. It's across the management stack after all and it also integrates with the wall sync project Which is how we as in chrono's in the background Deplicate data between persistent volumes of different clusters and this is what you would set up. It will continuously run the background There is obviously a recovery point a recovery time objective because it's as in chrono's, right? You know there are costs to these things so in a dr. Recovery case being so rare It's probably acceptable that there is some loss of data, but eventually It will be a feature in ACM To be able to carry over and the the workload if the cluster has failed and Restarted in the dr. Cluster in the very orchestrated fashion as in first-class ACM concept We'll get one question down in front Hi, I have a question about the conveyor, you know, I forget combating application to the Modernizing applications. Do you have any plan to support crowdfunding application because you know HCL? Guys offering support offering that services as a commercial services, but for the conveyor project Is it working? Yes Their tool is based on move to cube So they already they are most of their tool is already based on on conveyor projects So what what they're doing what we're trying to do with conveyor is try to address all the different scenarios out there With first of all answering your question. Yes, we do support that migration path move to cube supports that and HCL is using which move to cube to power their tooling. Oh, okay. All right. So that's that's that's the the whole thing Yeah, I'm gonna add one comment So the under the conveyor project they are moved to cube tool So we can use that tool to migrate from cloud foundry application to the Kubernetes So once you run they move to Q command right You got a bunch of the M of file based on how to deploy The manifest and Kubernetes like a deployment in a service is something like that You can also generate a bunch of the M of file for open ship the cluster as well So it's a still CLI stuff, but we are some working on to make it fancier. All right. Okay. Thanks so much There's always another answer and to add some some stuff on top of that We are currently working on integrating the move to cube experience on top of tackle So you will be able from your application portfolio Generate the deployment manifests that move to cube generates and have them fully integrated within your git repository So the whole idea is for a developer to push a button and have have everything in the repository for them to start Making changes and migrating the application and deploying it on the target flow platform from day one. Hmm. Okay Thanks so much So do we have another question right behind you Mike? You ought to be able to reach right there? Hello Another question on conveyor project. Okay. Do support other languages than Java? No, yes That's that's the The usual one for the moment. No for the analysis beat of it Okay, so assessment is language agnostic But for the analysis beat of it for the moment, we're focused on Java But we would like to bring in dot net and other languages as well, but we're still Waiting to get contributors that can provide that that thing So what we did with tackle to for example was to create an add-on oriented architecture That allows us to expand easily what we're building and what we would like to have now is Have other add-ons that can analyze different languages rather than them than the main tackle analysis having to do that So you don't have it on the roadmap yet? Sadly no We would love to have other vendors involved in that we have some involvement. So it might happen. I Did want to point out that if you go to conveyor.io you can contribute We highly encourage you to join the community. We will be happy to get your contributions and second questions about Multi-architecture clusters Is it possible or when it will be possible to have let's say x86 cluster with some worker nodes running on let's say power IBM power So at the moment you probably were here this morning we have x86 and ARM This is in the roadmap, I don't know exactly we can check that out When this coming this is coming but the answer is we will allow Multi-architectures in a single cluster and these two architectures won't be the only ones You know how far this is down the line and I don't know but this this will happen and how does it relates to? hypershift hypershift is a different concept with hypershift what you will have is One cluster that hosts multiple control planes in one cluster and then what you really want is Different worker nodes associated with every control plane Working against that cluster, right? You might I'm thinking about this now for the first time you may be able to combine that Why not because we're talking about two Openshift features, right? So Openshift will support hypershift on the one hand and Multiple architectures in one single cluster because we already support multiple architectures On the other hand, so you will have the tool how related they are. I don't know Maybe you will because you're gonna have many more ways to architect your you know your topology to design your cluster But it's two separate Topics at the moment. Okay. Thank you. All right One more question. I think this is gonna be our last question because we're hitting up the 5 p.m Thank you. I'm representing IBM client engineering, but as you might see I'm former Red Hat or so as far I remember the Openshift 4.1 was released like three years ago So my question is very simple any plans for the Openshift 5? I Think we have a lot of work with Openshift 4 at the moment Thank you


Hi, I'm Ross Tafsel from the College of Asia and Pacific here at the Australian National University. I'm here with Malaysian cartoonist Zunar, winner of the International Press Freedom Award and the Cartooning Award for Peace just last month on World Press Freedom Day. Thanks for joining us here Zunar in Canberra. Thank you for having me. Of course it's also some bad news in that you're being charged with sedition in Malaysia for cartoons and for tweets. This was the particular cartoon which you've been charged with sedition over. What kind of state were you in and can you explain this cartoon for us and why there's been such a strong reaction? This cartoon about the judgment on Anwar Ibrahim. Anwar Ibrahim is a very popular opposition leader and he's just been sentenced to five years to prison. People know this is all conspiracy and for me this the sentence is the motive of this sentence is to make sure to prevent Anwar from taking part in next election. And this this is Najib passing It looks like a very heavy political sentence. Yes, it is very heavy. This is why for me I'm very angry this is to show that how we cannot accept this type of sentence. And this is the law in the bin. The law in the bin. So for me this is all scripted, it's all a scripted sentence so that's why the Prime Minister like you said very heavy punishment. Very heavy punishment and you've certainly made enemies of the Barisan Nasional. In this cartoon you I noticed in a number of your cartoons you compare Malaysia's Prime Minister Najib with his father. Malaysia's second Prime Minister came into power in 1970 after the crisis of 1969. Why do you often compare him to his father? Because there are two very different characters. People love Tun Razak because he's the father of Malaysian economy where he introduced so many good policies. Whereas his son, Najib Tun Razak, destroyed everything what his father did. That's why I try to compare. Let's say as an example, Tun Razak he banned communists here. So he's an anti-communist, anti-communist. But come to Najib Razak, recently he banned activists. So he became like communists. So this is how I want this character, people to know those two characters here. In this one you've of course brought in the scandal, the corruption scandal involving Malaysia in the past few years. The 1MDB scandal and 2.6 billion ringer. Why did you choose here to compare him with his father? 30% for the Bumi, 100% for me. When Tun Razak introduced this is DEB, this is 2.6 billion. DEB is a new economic policy to give a 30% chance for Bumi Putra to involve in the economy. Previously the economic sectors are controlled by Chinese. That's why Tun Razak said we introduced this for the chance for Bumi to excel in the economy. So this is a very good policy. Come to the Sun, so Najib Tun Razak, he took 2.6 billion and 100% for me, 30% for Bumi, this 100% for me, for himself. So that's why I want to compare this to show that that's how you take for yourself this one he give it for everybody to share. Indeed the corruption scandal. This one I really like because it simplifies what can often be really complex if you read some of the media reports, The Wall Street Journal or The Edge. This one really simplifies and many Australians will understand the boomerang, the people's money going into the middle man's jolo. They come back to him. That's actually what he does, what he did, you know. Normally you technically put this money to another company and go far away and come back. I want to tell the people, you know he always said this is a donation. It's got nothing to do with YMDB scandal. He said that this 2.6 billion is a donation. It's not come from people's money. That's what he tried to tell. But through my cartoon I want to tell that this is our money. So that's why I make this picture for people to really understand. This is actually he put a comeback to him. So this is the purpose of this cartoon. In this one you've got Malaysia's strong man, I guess. This is Najib the strong man. Above the Lao Chin Chin Salatan, above the South China Sea. But underneath you've got here the international organisations. How would you say the international response has been around this corruption scandal? Because of the previous pictures show that YMDB money is located in an international bank all over the world. So now they all started, the foreign countries started to investigate money laundering. How come this money, the people can get so much money including step son of Najib in the United States. Which is they get the finance, the movie. So that's why the United States is investigating that issue. It's all about money laundering. He's already hitting Najib now. He is not strong. He looks strong from the surface. This is what this cartoon is all about. People in Australia may look, this is very strong. But in reality he's eaten up by this now. And do you think, I guess, is Najib the strong man going to sink? Oh yeah, it's a metal tank. I mean he's got two choices now. Whether he's going to become a dictator or he will definitely, you know, metal time he has to go down. So there's two choices now. You haven't just drawn Prime Minister Najib. In fact you've often drawn cartoons of the First Lady, Rosmar. Why is the First Lady fair game for a cartoonist like yourself? First of all it is an open secret in Malaysia. People know that she is the most powerful figure in Malaysia, not the Prime Minister. That's an open secret. People know that. So every decision by the government is actually she's her decision. It's not the husband's decision. So that is why I focus more on her. So she's the most powerful. So I make sure that even if she is not there, the ring will be there. The ring represents her will be there. So this is the thing. And she loves to shopping, of course. You don't have to shop. You have no problem to shop. People don't complain about it. Described as the First Lady of shopping in some reports. But you use people's money, that's what we are complaining. You can shop your own money, no problem. People money to shop. So this cartoon shows that previously they go to Hong Kong, Singapore, Paris, London, New York to shop. But now Hong Kong, Singapore, Paris, New York is about investigation. Right. When we talk about that they are very scared. And you've even got Sydney, I believe, here with the First Lady compared to the Opera House. And when people see the diamond, that's the First Lady. And when people see this little policeman with the twits and the handcuffs, what's that? This is actually the character of I.G.P. I mean Chief Police. When he appeared since March last year. When the day I was arrested, he used a Twitter posting to instruct these men to arrest me. So since then I make sure that in every cartoon I will put him with a Twitter. Which brings us to the last one and this incredible cartoon you've got. I guess this is yourself. Yes, this is a self-portrait. In jail. In jail with the Sedition Act, the Penal Code.  Is that what you're planning to do? Yes, that's right. This is the three laws used against me. Penal Code, Sedition Act and Printing and Princess Act. And I've got a philosophy here. How can I be a neutral? Even my pen has a stand. And also my philosophy of cartoning. And why pinch when you can punch? That is my way of doing cartoon. I make sure that, you know, I won't punch. I don't want to pinch. And finally, in one of the investigations, I told the police that you can bend my cartoon. You can bend my books, but you cannot bend my mind. I will keep drawing until the last drop of my ink. It's very inspiring, but you must worry. I mean, if you look at the case of Anwar, you know, the political sentence that was handed down to him, you must have times where you think, have I crossed the line? Should I pull back? We need to cross the line. This is a line of fear, we call it. It's not a normal line. It's a line of fear. If you don't cross it, you'll be trapped in there forever. So the system is very happy. The government is very happy if people don't want to cross the line. But if more, I know I'm crossing it. I hope more and more people will cross it. We have to break the system. If not, if not, they will be very happy. You can, now, you know, Malaysia has been governed by this party for more than 60 years. If you don't cross the line, we will give mandate for another 60 years for them. So, change will never happen. Zuna, thanks for coming in. Thank you very much. Thank you very much.


What we are today is a really a farm to bottle distillery Right where it's grown. We grow our own corn and sweet potatoes to make the signature vodka that we make today and it's a week we have a Tasting room an open area where people can come in enjoy and have a nice cocktail But we in the back is where all the process and takes place The magic happens in distilling my wife and I both are from this this area grew up in Small town north here between here in Mariana went to high school in Mariana and graduated from there and Have fond memories of Helena and downtown Cherry Street. My dad started farming sweet potatoes in In the 80s early 80s and farming back then got got really tough One of the things that was happening was you know You had to get really really large or at least large enough to sustain farming and row cropping Or get out and many farmers at that time got out of farming or diversified my dad chose to diversify So sorry for my sweet potatoes and farming sweet potatoes at a large scale I wanted to share with you though my granddad who actually bought our farm I Had been pictured here and then his dad was also farming on that land But he was a sharecropper never owned it and my granddad was also a sharecropper But he was able to buy that land so it's the same farm we own today I had the whole concept came back with my brother and dad and they had gone out to a farmers a vegetable farmers conference and he said these guys were doing some amazing things with sweet potatoes and this was probably seven eight years ago and Probably doing a time when you start seeing sweet potato fries and pies and turtles and torches and he came back and was talking so excited about this Sweet potato, but he also said that there was a booth at this conference that had a guy from North Carolina that was the one sweet potato bucket and my son and I had been working for a Long time I say a long time in the big scheme of thing. Maybe it wasn't we worked six months trying to perfect the product and get it right We went batch after batch after batch until we got a good combination of sweet potatoes and corn To make what we consider the best sweet potato bucket in the country I don't know. We just saw the opportunity and I will say my dad Always did say hey if somebody's doing it you can do it too. So we just figure out a way


Bitmap Brothers is a UK-based developer that primarily made games for Atari ST and the Amiga. They had a hand in only two Super Nintendo ports, the first being God's, a goofy action platformer, and Soldiers of Fortune, or as it's known in the UK and in PAL regions, the Chaos Engine. This is a top-down 8-way shooter in the same vein as stuff like Smash TV and Zombies 8 My Neighbors, in particular the latter since you're navigating mazes and collecting all sorts of stuff. The story has this evil guy named Baron Fortesque commissioning a scientist to build a massive steam engine to transmute matter called the Chaos Engine, and of course the guy goes crazy with it, people are turning into werewolves and monsters, and six mercenaries must band together to stop him somehow.  For example, you need to activate these electrical nodes in order to unlock the exit of each stage. The number at the top of the screen is how many more nodes you need to find. Some stages only have one, but some have three or four that you have to track down. There's four worlds featuring four levels each, but what's unusual here is that there aren't really any boss fights, there is a final boss, but the name of the game here is just survival and finding the exit somehow. There's no time limit so you can wander around as much as you'd like, and there is a password system here as well so you can pick up where you left off. That may sound simple, but Soldiers of Fortune does have a lot of extra stuff going for it. For example, there's six different soldiers you can play as, each with different weapons, and four varying attributes that you can level up like skill, health, speed and wisdom, with the green showing what they have to start with and the blue showing the maximum amount that they can level up in that area. The skill meter governs the potential of the other attributes if that makes sense. If you increase your skill, then you increase your ability to earn points in the other three areas. Health and speed are self-explanatory, but the wisdom attribute brings me to the next thing that sets Soldiers of Fortune apart. Even if you play this one single player, you still have a computer AI-controlled partner. You choose who they are from one of the five remaining characters. They roam around and shoot stuff on their own, they have their own health meters and power-ups and everything, and the wisdom attribute determines how smart they are in terms of dodging and avoiding enemy fire, as well as how far ahead they can see enemies. Now, when you have an actual second player occupying that spot, the wisdom attribute is pretty much useless, but for single player purposes, it's pretty cool. I should also mention quickly that when playing with a second player, you stay locked onto the same screen together. There's no split screen here. Yet another thing that stands out about this one is the opportunity to buy upgrades after each world. You collect coins for each enemy you defeat, and enemies respawn constantly, so you can really load up on cash here, depending on the length of your attention span. Once you've got some money, you can buy health, speed, skill, or a number of special abilities like bombs, health, mines, stat boosts, and even a map so you can see the entire area. Also, if your henchmen died in combat, you can spend 500 coins here to bring them back. Soldiers of Fortune is sensibly structured. The mazes may get old and repetitive at times. There is a lot of wandering around and backtracking here, but at least if you die, you start again at the last ying and yang icon thing you've picked up. And there's plenty of those in each stage. The closest comparison to this one is probably Operation Logic Bomb. That's another top-down shooter that emphasized angles and the use of some unique weapons. You gotta approach this one the same way, using whatever is around you for cover. What's especially nice is that you can use the L and R buttons to aim without moving the D-pad, and that'll really come in handy to help you progress. I will say though, I don't think Soldiers of Fortune has laid out as cleverly as something like Operation Logic Bomb. That game had a better layout and some great weapons, but Soldiers of Fortune outdoes that one in terms of options and replay value. I love all the characters and the leveling system, even if it kinda doesn't matter what you do at a certain point, and the special weapons and power-ups are great too. Even the computer AI-controlled partner is pretty cool.  If I were to nitpick any flaws here, it's that the sound design is a little flimsy. For a game where you shoot anything that moves and make tons of stuff go boom, I would have liked to hear a more satisfying boom. I don't know, maybe that's just me. The music here is fine, but the sound effects could have used some work. I should also mention that this is available for Sega Mega Drive as well, but it's pretty much the same game. Some might like the wider resolution the Mega Drive provides though. There's also a sequel called Chaos Engine 2 that was only released for the Amiga, but I have not been able to play that one. I have played the remastered version of Chaos Engine available on Windows, Mac, and Linux, which is a lot of fun since it retains the graphics and audio of the original, but it's blown up to 16.9 and you can play multiplayer online. As for the SNES game, on the surface, Soldiers of Fortune or the Chaos Engine appears to be a very simple game, but the different characters, attributes, and henchmen give the game some replay value. I will say the mazes do get kind of old after a while. It would have been nice to see something different thrown in there, like some boss fights, but this is still certainly a worthwhile playthrough. I was really surprised because I was totally prepared to be really angry at my AI-controlled companion, but he was actually pretty useful. So yeah, I would check this one out. I'm not sure it's going to compare to stuff like Zombies 8 My Neighbors or Super Smash TV, but it's still pretty good, and it puts a subtle twist on the usual top-down action genre.


Hello and welcome to the session. In this session, we will discuss the question which says that the x coordinate of a point p is twice its y coordinate. If p is equidistant from q to minus 5 and r minus 3 6, then find the coordinates of p. Now before starting the solution of this question, we should know a result. And that is if the coordinates of point a are x1, y1 and the coordinates of point b are x2, y2, then by distance formula, distance a b is equal to square root of x1 minus x2 whole square plus y1 minus y2 whole square. Now this result will work out as a key idea for solving out this question. And now we will start with the solution. Now in the question it is given that the x coordinate of a point p is twice its y coordinate. Now let the y coordinate of p is y. It is given that the x coordinate of a point p is twice its y coordinate will be 2y, point p r and y. It is given that the point p is equidistant from the point q and r whose coordinates are given to us.  Now since p is equidistant from the point q and r, therefore distance pq will be equal to distance pr. Out the distance pq and pr when we use the distance formula which is given in the key idea. So this implies now the coordinates of p are 2y and y and the coordinates of q are 2 minus 5. So the distance pq will be square root of minus 2y whole square plus minus 5 minus y whole square. Which is equal to distance pr and here the coordinates of p are also given to us and the coordinates of r are also given to us. So the distance pr will be equal to square root of minus 3 minus 2y whole square plus 6 minus y whole square.   Now this implies answer with each other is 29 plus 10y is plus 2y minus 9 minus 36 is minus 45 is equal to 0 is equal to 0. 16 which further gives i is equal to 692 which is equal to 8 2y. Now we have the y coordinate of p as 8 p will be equal to 2y which is equal to 2 into 8 which is equal to 16. Therefore x is equal to 16. Hence the question and answer for this session hope you all have enjoyed the session.


So what do you think will take your jobs next? No, it won't take your jobs try generating code out of it and see you'll feel more secure about your job So so that's the thing. It's it's more. There's a lot of it's called FUD fear uncertainty and doubt whenever new technologies Come out right like and AI is also one such one such technology and AI curriculums have been there for everybody who studied from 1960s 70s 80s and even right now Okay, but the thing is every Every level of computer science is built with different kinds of abstractions than what the previous generation so perhaps when let's say if you are If you're in the older side in the crowd like me, okay You might have studied right from assembly programming and various things in order to learn what how computers work and People before me might have studied the electrical circuitry what happens and how computers work So if you take a look at the generation now who are in the colleges They might be learning prompt engineering in order to generate programs or anything But the thing is end of the day you're doing the same things like you're coding you're building your applications You're deploying your applications the guy the way in which you generate our code is going to be different And also the way in which these applications are going to be deployed are are totally different So let's understand the code behind these products. Okay, so how many of you have? I mean like how many of you use open source in day to day project delivery deployment everything, right? so It would be fair to say that open source software has eaten the world right like 99% of the So code bases would have some some footprint of open source software within them. It could be building tools It could be compilers. It could be Various other scenarios and like linkers nobody like nobody really studies Compilers linking loading any mode but all of these things happen in the happen in the back end and also to add to this complexity the number of new Software architectures and hardware architectures are also coming out like if I take a look at How it was it's like a full cycle right like when I started my career. I was working on Spark machines, okay We had we had to install Linux on spark machines because the Particular client or customer wanted more freedom out of the underlying hardware and if you take a look at the generation right now Again, people are writing code to run on secondary architecture machines Which are arm based and all so the kind of problems which are changing are also going to be different and you're going to start deploying your applications to specialized Specialized scenarios because the new new markets keep keep on coming out right now space tech is one of those markets, right? Application developers are writing applications which can be deployed onto satellites right and these satellites are powered by solar panels Which have low which which have low power, okay? So it is very if it is very interesting to see what kind of programming language Also, you choose in order to write your applications Which are going to be powered by low-powered devices and the same thing right now if you take three or four cars together The modern cars which are coming out you can have a very good GPU processing Data center right like that's the amount of chips which are getting into devices like cars and all of those things and maybe even the consumer side of things would be Even better. I don't I mean I hope they don't take it very fast saying that it predicts accident is going to happen in Three seconds, please pay for the airbags to be activated So hopefully it doesn't go to that extreme, but it could be okay because they can they can they simply can so these are a few of the things So what I wanted to say is open source and AI technologies have a similarity which is going on right now, so Backed like if you go flashback 20 two decades ago When people wanted to run open source software within their own Organizations and within that it was a very tough battle So most of you have fought those battles within your own organization where you wanted to introduce certain open source components When there is a proprietary alternative out there like it could be a database. It could be a programming language It could be It could be the architecture of the systems itself Like for example, if you wanted to move out of secondary architectures and wanted to come into let's say Intel sys based processors and it would have been a battle for most of you out here and These kind of battles are being fought a day in and day out, but take a look at what open source has done to the industry right now, right? It is very at least in the cloud in the infrastructure software world open source has won its battle Okay, who is writing code in a proprietary programming language right now? Most of us are writing code in Python Ruby go rust and for the satellites that using OCaml like like these languages, right? So what has happened is the similar battles which the open source the open source community went through the artificial intelligence Community is also going through the same the same kind of things people are thinking should be tested generate a way I within our workflows is it safe for me to put my Customer data or my company's process data on a third party hosted AI service Okay, these are the kind of doubts with CIOs and even developers have is it safe to run my is it safe to run My code and we have already seen so popular software projects like Apache if you generate the code nothing wrong with generating code with AI tools but AI is very good at repeating human mistakes So what mistakes humans would do in the code? It's been trained on the same mistake and it can generate accurate mistakes what the humans can do Okay, so what happens with that is every time you try to generate code like so you can go to chat GPT and ask Okay, write me a JWT Token with go programming language. It'll generate within next five or ten seconds It's going to generate the code out of it But the thing with a problem with that kind of a code is it is not domain specific under what context you're writing the code Right. So those are the things which are going to be More more and more we do have a tool which we'll talk about later called Ansible light speed you need to be able to train the tools for the context in which your Which are language domain is whichever which are business problem is that's where AI would get its strength So all the repeated tasks what you're doing, that's where once you start training the training these models more and more It is it is able to do things better. So and let's say for example, the code which is being generated I mean it could be using a older library because it might have been trained on a library which is Two years old or a three years old and it might have that kind of a domain knowledge And the second thing is the coding style. So major projects like Apache are Clearly mandating that if you have generated code with code generation tools point us to the origin where this code was trained on Okay, we we will not just accept the code the way it is, right But any day like like say certain tasks are becoming better and better most of the developers Find using generative AI tools to write testing for test coverage generate out the test cases and all it has been It has been enabling a lot of developers But it also like I said it is going to create more jobs because you need to sit and verify the code which is being Generated again. So so the it's called like Jevons paradox like it's like I have to give you an example in Bangalore How much ever wider the road they make people will buy enough number of cars to fill up that road Okay, so how much of an infrastructure you have within your systems? Okay, you're going to run more and more containers and VMs in order to fill those systems up, right? And the problem being at one generation like when I used to write my code I never used to think about the cost right because most of the customers which I worked with the client used to have their Own private data centers, okay, but now think about the code what you're writing all of this code Has a cost associated to it, right? What is the cost? I'm not only talking cost in terms of the actual price Yes, you're paying for the actual price the CPU consumption the network consumption the storage consumption of the code What you're writing all of that has a cost depending on the cloud Wender and it's also very dynamic in nature. The second kind of cost which I'm saying is why did you introduce Golang into this project, right? And why did you introduce a particular framework into this project? So what happens with that is? You are consciously introducing something some cost in order to get a benefit out of that, right?  Yes, you're getting the You're getting the benefit of concurrency. You're getting the benefit of You're getting the benefit of benefit of multi-threaded and writing more efficient program The flip side of it is End of the day, it's humans who write code and people tend to move jobs or people tend to move out of the project How easy it is for you to find another good developer onto your project, okay at that given point of time, you know the Sticking with the programming language like Java or Python where you can get people productive or you can get a person ready at any Even point of time is is cheaper So every time you think about any decision because what's going to happen right now is lot and lot of work is going to be generated The workload is going to take the AI systems are going to take a lot of this heavy lifting You need to be a judge of what cost is this introducing into my system For some it would mean like say if I'm generating a leave letter to send to my manager Fine, nothing would happen like you know taking leaves from from from your boss is a very common thing It is not competitive information, but Completed information would be if my top customers if you if you need to say generate something for a top customer And it using a third-party service They might be a problem where one it might not be legal if you're using these systems The second thing is it might be legal in your country But illegally in another country and it can also have its own biases which can creep in on to the customer data So so one thing what is going to happen is When you're introducing systems like say chat GPT or any other a Bard or any other AI system Many people are excited about this technologies But you as developers or architects need to need to be sure why you're introducing these technologies into your workflow Okay, because the amount of code being generated can become more that would again mean the amount of Infrastructure you're going to consume is going to become more and more. Okay, so we developers are very good at Inventing problems or reinventing the wheel. So it is a common thing like if there is a editor Okay, so it's it's a simple thing developers like to reinvent the wheel. Although we say, you know, they want to be productive Yes, they want to be productive, but they get the kick out of reinventing the wheel like say Just to write YAML scripts properly for Kubernetes There are about 20 editors for that because they didn't like the way how the previous editor implemented that Okay, so that's a power of open source If you don't like a way in with something is implemented you can go ahead and implement your opinionated way of Architecture and the same thing is happening with AI because you might not like the way a vendor is doing stuff So it gives you It gives you a position where you can build your own AI models based on open source So what we envision is people are going to run sensitive workloads on their own private clouds and also for the elastic point of view They're going to move certain of these workloads to the public cloud Right. So AI needs hybrid cloud like say for certain sensitive data on and right now There are certain chips which are managed been manufactured just for certain Computational tasks. So you have certain kind of GPU certain kind of for TPUs which are being done only for certain kind of tasks and having your own private data center and a public Cloud cloud provider to work in tandem is going to become very imperative and this is one of the skills which most of the developers need to Will start building and this is also one of the skills even the operation operation sites operation folks within your organization will start building and Now you now you hear about DevSecOps, right? So next year it would be ML DevSecOps So because you want to have the data scientists your developers and also your operations people working together Securely securely delivering the models and securely running their code pipelines to delivery So like so what Red Hat's opinionated view is like so what we did for Red Hat Enterprise Linux So over the past three decades, right? So how we curated the open source communities and how we curated the open source projects which will become popular when I mean popular is Can somebody take a guess how many open source projects are out there? How many of you think there are one million open source projects? 10 million 100 million Right, there are 100 million open source project if we take github and various And various publicly available source code repositories that there are so many open source projects of these open source projects Only 20,000 come into a mainstream distribution a mainstream distribution is something like a Fedora Something like a Ubuntu something like opens user and of these 20,000 a word only 10 make it into a enterprise Long-term supported thing. So why do you think that one happens, right? It's not because those projects are bad, okay? Most of the open source projects die very fast because they don't get a community around them, right? They don't get contributors like tomorrow any XYZ company can say I'm going to open source my project open source in projects Is easy even you can open source your code base in get a putter Get a proper OSI recommended license and make it open source But how do you build communities around these projects? So what reddit does is it's continuously you see the vast number of open source projects out there and most of you might have seen the CNC of landscape page So if you are a developer or an architect if you take a look at the CNC of landscape page You will be overwhelmed by the number of choices you need to make right So what reddit does is and what reddit is good at doing is it starts participating in these open source projects Well, there is a active communities around it and starts contributing. It's not just Contributing in terms of the code. It also is contribution in terms of the governance of these projects, right? Because many people are using it. There's a active community Active community means that a company like red hat can provide enterprise support for these open source projects for a longer period of time So red hat enterprise Linux has been very successful in doing that for Linux and another way another ways The number of applications which are being written are becoming more and more and this problem is being solved by Kubernetes the way in which you can deploy applications the way you can scale out where you can scale out to multiple clouds and We have done the same thing with the open-shift Container platform and open-shift container platform Lay I mean early this year has got more than one billion dollars of revenue and annual recurring revenue So that's how big the enterprise market is and that's how much the customers are willing to pay in order to have a true hybrid Hybrid model in order to deliver the applications so So what we've been doing with all of this is we've been trying to get all of the popular opens a popular AI projects ML projects working together along with your application Application platform which is powered by Kubernetes and to be delivered on a solid platform Which which is based on rel core so that it can run on multiple edge multiple points either the private data centers your edge nodes or even the or even thing so More and more open source projects in a ML are going to be adopted Few of these projects can become enterprise companies by itself because these are going to be a domain specific way and Open source is going to win the battle again in the AI and the ML space right now There might be few companies which have which have gotten a lot of market share But they have got it only in one segment of AI. It's not the whole thing. So this is where companies Are going to use more and more open source projects one in order to protect their data in order to protect their models in order to protect their In order to protect their choice and freedom what they have with their thing. So they're going to build specialized models on specialized hardware for solving their problem or Companies who want to try out like say maybe a large bank or maybe a large system integrator who want to try out This technologies are going to leverage public allowed because they want to touch the waters And they should have a way in which they are sent their private data and sensitive data is on prem But where they want to get where they want to leverage Artificial intelligence they can provision out compute on on public cloud and test out these data So open source watch out for a lot of open source project good open source projects Which are coming out like Lama index and various other projects which are coming out in order to? I mean in order to implement your Cloud and infrastructure workloads onto public loads. So this is how we do it So that's a thing the open source and artificial intelligence and machine learning The true way of in which large enterprises are going to consume is going to be Hybrid in the near future. So what we'll show after this is we'll show We'll show a platform where we are integrating because Enterprise customers have already invested in rel. They have already invested in open shift We are showing a way in which you can get or even your data engineers the data scientists work with your application developer teams And do the projects in tandem use the same infrastructure because just because a new technology came out the companies won't ditch their present Infrastructure and start a new infrastructure or something they are going to leverage their existing investments And with these existing investment with a platform like OpenShift you can have a true hybrid hybrid platform so So and like for example one of our biggest customers Delta They moved from their own private data centers to public cloud with the red hat OpenShift on AWS They were able to move 90% of their infrastructure because of OpenShift's power in which it can run on any cloud And also keep in keeping in mind the SLAs and SLOs in place They could better serve their customers once it went on the public cloud So the customers can keep on they can come from Private data centers to public clouds and larger customers also would would want to come out from public clouds back to private data centers It could be driven by the governance within that particular country or compliance laws which they need to which they need to are there too Like for example two three years back Mastercard and MX were Given a mandate that they can't issue new credit cards in India because they were sending the data outside, right? So until they could get there until they could get their Servers within this geographical locality They can so what does it mean for developers? Does he does he have to change the code bases? No, the underlying platform needs to handle all of these kind of changes and more and more even in India They're coming up with a personal data privacy bill and many countries are coming out with this Regulations where the data? Especially the financial data and the sensitive citizens data need to be within the geographical boundaries of that particular country or region GDPR is one of those examples So all of these things would work on OpenShift with with service mesh in place and also your data Your data pipelines in place and we'll show you how easy it is to set up the data along with your applications and deliver these Applications so you always have your ML teams working with your application teams and also your ops teams to keep on delivering these projects So this is the projects like developer hub will also be Easy in order to onboard your data scientists and ML engineers along with your application teams Who are already been onboarded and deliver your models and applications to them? I'll stop over Later in the afternoon. We have a dedicated talk on Ansible light speed. This is I mean this project has been trained only on Ansible scripts, right? It's not a generic Generic generative AI project So this project is very specific to a particular domain to check configuration diffs and also to make it very easy to have a Consistent ansible templates being generated So like these domain specific tools are going to be the future of cloud and infrastructure platform or air tools in Cloud and infrastructure platform. I'll stop over here and I'll give it to my colleague Irritation here will take you through a demo. How many of you work on jupiter hub notebooks? quite many right and Okay, that's good. And how many of you are actually doing machine learning? I think everyone right who is Okay, I see more hands than last year now So that's I think next year. I think everyone will be actually doing some sort of I have my red-eyed open-shift data science Right now it's called red-eyed open-shift data science. The new version is going to call be called open-shift AI so the moment I I Click on that. I actually get this open shift AI UI right like you saw the developer hub UI. This is an open shift AI user interface what you get when you actually log in into open shift AI Now you see here there are projects which we can set right like for example, I can create a data science project say DevNation project right 2023 So this will actually go and set up a project in my open shift here I'll actually set up a project blank project in open shift Right and go and interface everything is all Kubernetes native here The moment I set this up then I can actually create workbench So workbench is basically you actually say what kind of Jupyter Hub notebook you want and What is the configuration you want what kind of version you want right like for example? You want to use spy talk or you want to use TensorFlow or maybe you want to use open We know from Intel right okay all those things are built-in like we also are going to support trusty AI as well as part of your Workbench so you can actually create a workbench So DevNation Workbench 2023 right so I can select which image I want right so there are predefined images for example, let's say PyTorque I Can select my container size and I can actually select so this is all a data scientist Who is who wants to work on and interface everything with open shift right or a Kubernetes application platform? Assume that that's a data scientist was actually working here and everything is built in for that Including actually creating at the inner loop pipeline right where you actually pick up the data Do your training testing and then create the models push the models in as part of your inference Right if you want to actually so those models so everything is is here built in here for you as a data scientist So you don't have to go anywhere now. I have configured this environment with NVIDIA GPUs so currently We have NVIDIAs and we also support Intel Habana Gaudi Accelerators as well. So those are also part of the Support so those things you can actually get configure those kind of servers as well and get it Discovered by OpenShift Data Science or OpenShift AI. So let's say I want to use the accelerator here so I'll use one and Now I can actually define what kind of storage I want to connect Whether there is a user data connection, which I want to do or I have an existing user data connection, right? And then just create a benchmark So this will actually go and create a workbench for me it will start a Jupyter Hub notebook for me if the resources are available and So that is what I will see here right there are three different projects. I will see Now this is an interesting one Which I wanted to show and I'm more excited you all with the racing game, right? So this is where we actually will see a Generate AI based Jupyter Hub, which I will actually pass through and What we'll do is we'll actually convert text to image. So let's say I have an image And I will just say whether that image should be on a beach or in a mall right or in a house And that will actually generate and give me that specific image with that picture in the background. So that's the Basically a lot of defect images, right? It's one of the Use cases you can see here, but more mostly aligned with the generative a concept here. So this is my text-to-image Demo here if you see I have everything in place here, right? I have this workbench which is created I have my Jupyter Hub notebook here running. It shows me what packages. It's running here, right? This Lira is a very interesting component in this What you can do is here whatever Python programs or you have right or our codes you have you can actually align and Create boxes here like I did here So you can actually set up a pipeline like this with all your codes Okay in in pipeline and just run this pipeline This is most likely you are setting up a pipeline for inner loop, right?  So it will actually run the whole pipeline for you right from Getting data doing feature extraction doing training. Okay, then doing model generation even hosting models as part of model serving right everything will be built in part of your Pipeline which you don't have to do anything as a data scientist It is already done for you as part of and this actually the good part about this is Lira will convert this into tecton tasks And automatically push it into OpenShift data science or OpenShift platform Okay, so even the data science you don't have to worry about creating any pipelines or any tasks Just push your code here connected provide the inputs which it needs as part of that for example This particular task Needs some of these parameters which are listed here, right? So just provide those inputs and I think you are good to go as a pipeline, right? So I was here. So this is my workload. I have my cluster storage configured I have my data connections here, which are all object storage. This is where Text to image is where all the images are loaded here and I have my pipeline artifact object storage We can use any type of object story like OpenShift data science OpenShift data foundation for example Which is again a container storage. So any container storage can be used here To connect and store your pipeline artifacts So this is my pipeline which I have created which you just saw right? So the moment I run this pipeline it will go and create a tecton Pipeline for me. So I had already created one here, which got completed And this is where I have my models. So this particular program I created four models yesterday night It took me like 30 minutes on a 10 GPU single single GPU, right? So that's that's a that's the time it took So as Ramke mentioned, right? Genetic UI is there but it comes with a lot of cost, right? If you actually want to do your own model creations like The LLMs, right? The Lama 2 and all they are trained with like 7 billion parameters And I think 12 billion parameters, right? It's it's not a small thing to do actually it takes a lot of Infrastructure and cost to do that actually So what a lot of data science is what they do is you actually take that whole Lama Model you can just strip off the initial layer and Push in your specific content and you can build your model Okay, so you don't have to go through the whole model training and model creation You can just peel off the upper layer and try to embed your specific Whatever is your specific domain content, right? You can actually Add into that and create your own model out of that. So a lot of data scientists nowadays are doing that So here is my there are four models which I have I need all these four models for me to generate Text-to-image So these are the so I get to see what are the pipelines are set up here I go and select this I get to see okay. These are the three stages. I have in my pipeline I do experimentation then fine-tuning and then remote inferences and these are my runs here The ones which are triggered. This was a triggered run which ran successfully If I select this there was only single task ahead here The run output it shows me what are the details here and if I go to my pipelines, right? that's the Text-to-yeah, this is my pipeline which was ran successfully So that's what you see it in the open-shift application platform here. So let's say this is my So it's This is where I use NVIDIA a semi that's the command to actually query and see whether you are using GPUs or not, right? Now this command. Okay. This is where I'm trying to So we have a specific dog right which has a reddit hat here. This is a dog with reddit hat here And we actually want to ensure that this is the image with gets displayed with the background I want to suggest for example Let's call this reddit reddit teddy, right the dog name. So reddit teddy should be on beach So if if I see this the output should I should see an image of reddit teddy sitting on a beach, right? so that's the whole point of doing generate UI and In this case what happens right when I'm doing the experiment initially I get the dog faces, right? Very cute ones, but I don't get this reddit teddy Right, I see various different types of dogs if I'm doing training So what I'm trying to say is you take for example llama to similarly you take this particular Algorithm which we are using from hugging face. This was created by one of our colleague from open-shift AI view Chris The whole source code watch what we are seeing right now the use case. So he actually used some of the readymade hugging face Models and that's what we are using. So that's why we are not seeing our reddit teddy, right? Because it was not customized to my images Right. So what I need to do is I need to actually fine-tune So I need to have reddit teddy images. I need to make it learn right and Build a model out of that. So what I do in this particular fine-tuning stage is where I actually train this model Okay, I'm doing a training here this the training part I use some of the accelerators here to actually do the training okay using a training Dreamboot again, and this is a program Python code from hugging face which we actually use here and This is the training which actually took like 30 minutes on for this specific images so I did an 800 epoch and Like 200 images were trained From the reddit teddy to understand that yeah This is reddit teddy an image and then different images which needs to be catered to right to actually give you an output Okay, which needs to be like merged with your reddit teddy so this Once I finished this right it actually I export this into an ONNX format Which is a runtime format which we support on OpenShift AI This is the format which I convert my all my models into and it gets installed into my Storage, which is the AWS storage which an object storage So Yeah, this is where I actually move it and store it into S3. So you see all this and Then then I upload this into an S3 Storage once this is there right I actually Need to tell OpenShift data sands that you need to do a model serving using the four models Which we are generated and then using those four those four models we can then Go through the third Jupiter hub, which actually does inferences, right? Okay, exactly get the image understand the text and build generate a new image right based on those two combination parameters so this is where actually I Create a serving runtime I Actually use a custom model server. I used Triton here I think you can see the Triton image. Yeah, I see the Triton Image here. I create a Triton server Which is again a runtime to run the ONNX models onto that and then I actually push These four models which I have right from my storage You guys it's visible right back in the middle. Yes. No Yeah, okay, cool. I just wanted to check whether you're attending your identity or not, right? Good. Thank you So yeah text to image basically this is what I'm using at the path This is where my models have been stored on the S3 bucket and There are four models But they are doing different functionalities that is combined to actually provide you one outcome and this is my inference here so let's say currently This is you see this this looks like Teddy, right? Yeah, so Teddy is right now in a mall and I want I had trained it so that a dog image is Teddy so in a mall. So let's say I want to Make that on a beach for example, right the moment I I run this this is nice thing about Jupiter hub notebook like you can just write it in a night you can run it in a stepwise mode in this and Get the outcome so this will run for a Minute or so. I think it's using GP in the back end to kind of like Create the image once the image is done. You should then be able to see Whether Teddy is actually on the beach or not, right? So that's the image. I expected to get generated Okay, so how many of you have used open data hub anyone using open data hub? No, right. So you should definitely go and check out open data hub or maybe you should actually go and Explore that because it's it has some nice cool tools for you to use Here our Teddy is there on the beach, right? Okay, so I can say Teddy is in my house, right? So it will actually have apartment photo sitting behind that, right? So that's the generative Yeah, we are talking about here. Now what I do is let's say instead of Teddy Right, I want some other image to be trained and a new model to be generated, right? Every time I need to keep Pushing that fine tuning Pointing to my storage right instead of that I can parameterize that so that and then build a pipeline So what I do is I'll actually have this pipeline here Which I created using all these three. I mean you can use any depending on what kind of How you are generating your models, right? How you are actually getting your data Doing your feature extraction and fine-tuning and training and things like that You can actually in between. I don't have this model serving Thing yet, but that is a task which I can add as well so that the models are served locally on the inference and then You can actually use the third remote inference notebook here to actually get the inference like we did Now let's say I want to just run this, right? Okay, and say text to image. So this submitted a Tecton pipeline right into my OpenShift data science. So here that is this the pipeline gets created. These are the Okay, I see These are the tasks. I am running. This is experimentation. Then I do the Training and then finally the model serving part, right? So this is how you actually convert your Pipelines into real time like real Tecton pipeline. Okay, which gets executed and you don't have to worry as a data scientist Whether to configure everything, right? It's there, right as part of your editor. You can actually edit and create this pipeline and Just boom. Yeah, you just Executed and you have this pipeline ready running in your OpenShift pipelines, right? So it actually leverages and it's tightly integrated with the OpenShift application platform here, right? So So this is where you see the run output and you see all the runs here Now I mentioned model serving right where and actually these are the four models being served here and these are all Internal services. We are using gRPC. You can actually host this as external model serving also if you want to do model serving as a service offering, right? Okay, you can actually do it so everything can be an API connect using RESTful API and Like I do a gRPC. gRPC by the way works very fast compared to to The REST API very very fast actually. So that's why because this is all internal in-house into the same Environment, otherwise you can also use REST URLs like we have it here or you can externally connected through TLS extended Security and you can host your inferences and just do a API RESTful call and it should be able to call this Model and should be able to give an output to you So that's what we are doing here and this Jupyter Hub notebook The third one, right? So we are actually if you see here in this initially So we are yeah, so we're actually calling up this model serving It's port and these are the four models which we have hosted. We are actually calling that if I go to open shift, right? in my Workloads So I have this model serving and it has the gRPC port 8033 being mapped here So it's going and actually calling this and the inferences are running within this particular model serving so you can create our own model servers and you can Depending on what is your runtime for those specific models you can have customer model serving also for your specific requirements So, yeah, you like the teddy on the beach Yeah, sorry can't hear you. Oh Yeah, of course, so that's how you can you train yours your image and say you are on Eiffel Tower It'll actually show you an effort. All right That's a good use case Okay, I think I Think we have some time to take questions on OpenShift UI and Yeah Sorry, can we have Mike please that you can have your own notebooks and you're serving on times and all My question is is there a free tier available of and how is it different from Google's collab? Google's Colab Colab, okay We have any trial Yeah, yeah, so once you go to the sandbox which has shown in the mornings You can go console.redhead.com. Sorry developers.redhead.com So in in Ashwin's talk, you might have seen how to get access to developer sandbox So once you go to the developer sandbox The one so you can here you can start your sandbox for free So it'll take you to the customer portal But will I get a GPU option for the free tier? Yes, I need to check because they keep changing So, yeah Yeah So here there are different You can you can bring your own scenario or there are different scenarios out here which you can try Yeah, basic learning these scenarios are good enough like you launch your open-shift data science You create a PyTorch model TensorFlow model And you'll be doing all of this Activities hands-on on the open-shift data science sandbox. Sure. Thank you. Yeah, but it's not Really powerful enough GPUs so that you can mine bitcoins or something Training models and all you can Because we get I think a hundred GPUs you can run in parallel you can run two different Yeah, and that's something which I keep saying that you need to reduce the crypto I'm sorry cognitive load check for your servers and infrastructures passwords at least To be secure because one of the most common thing is then Especially the free tiers and all which comes people use it as what instances to mine for cryptocurrency So, yeah, so always secure your own systems and even your own laptops So if you are thinking of getting your data scientist Let's say you have an open-shift platform or Kubernetes platform You want your data scientist to actually be onboarded on that platform, right? Like your developers are so that's this is the best way to Actually start either use this or use an open data hub Okay, then they are right on your open-shift platform and they will not even realize it. Yeah So the thing is the data scientist wouldn't know what a CICD pipeline is or how he needs to manage a school What happens is he's just working with us Jupyter notebooks and all the application pipeline is being created in the back end and securely being deployed to the Target environments the same way the application developers are not sure what ecosystem they might or might not be sure what what tool sets and tools which the The data scientists in their organization are working, but they need to use the results from their From their output in order for their applications to be served So both of these things work in tandem like see let's say for example if you say the application developers would understand How a git workflow is okay, but a data scientist might not work Might not understand what a git workflow is and how do you do like git buys because their tools are totally different They're going to work with Jupyter notebooks are some other workspace like that So that way onboarding a developer ML and ML engineers and your engineers is very easy over here because they are not burdened with the cognitive load of learning how an application pipeline is being deployed They are just working on their own models. If they know it is great, but not yes Developer hub it becomes easy for developers to be onboarded right the same concept which goes here as well and with Alira there are a lot of enhancements going on with Alira so that the the whole of inner loop pipeline right the data science Specific or ML specific pipelines You you don't have to the data scientists don't have to worry Just drag and drop their Python codes or whoever is actually managing that whole thing right for example a data scientist They can just collate Just just pull in the Python codes from the data engineers from the machine learning engineers right and then the application developers And then just build their own pipeline to be deployed right into your production Yeah, and now this product name has changed from yesterday night We got to know it's open red at OpenShift AI OpenShift data science is part of OpenShift AI and what so it's Yeah, thank you. Basically, it's it's your code to deployments. I'll take in care from the single platform So I can't hear you So it essentially is code to your deployment. It's all While using your ML engineers also into the same. Thank you very much So like like I said if you have ML engineers within your organization Like develop developer hub you might have to write what tools and What are the things which they they need along with your application pipeline so your template might be a bit bigger But you can on board both the teams together on to product because What happens is in many organizations, especially the enterprises most of the developers might already been on boarded on to OpenShift kind of a ecosystem right there. They're already They are already in their Kubernetes pipeline, but getting the Getting the ML engineers onto this pipeline is very difficult So by using the developer hub template along with the underlying infrastructure, which provides everything It's easy for you to on board even the ML engineers on to the projects Yeah, we eventually we can have a Jupyter hub link there, right? Whatever you've seen the OpenShift application platform from the OpenShift AI route perspective right for Jupyter hub and it gets created as a workbench eventually we can actually have it as part of the the developer hub right But that's I think something Moet was mentioning that they are thinking of Yeah, right and on developers.radar.com is not just OpenShift most of our products So you can find the trials over here or even sandbox versions over here You can use it and I'll get like let's say most of our licenses are very portable Like for example, if you want if you're provisioning your OpenShift infrastructure in AWS in another zone on ARU You can take you can take your models from here and just deploy it out there You can try it out here in the sandbox and then you can Most of the sandboxes are available for 30 days But we have a slack channel if you want to extend it out there or if you already have Your compute from one of the cloud providers you can just export it onto that cloud also after you do your experiments Any we can have one more question if there's any other question any further questions Okay, I think then yeah and and also maybe in the evening we can cover we also have a slack channel So if you want certain kind of sandboxes to train up your teams or onboard your teams So you can come and request over there and we can depending on our cycles We can create the scenarios or we can create the environments. So there is a last track. I am doing here Again, that's more to do with GitOps and how it plays a role, right? But I'm actually going to touch up on ML ops through GitOps. So if someone is interested Yeah, please join that talk as well. If you are more interested in ML ops to be sure Okay, I think thank you for joining. Thank you for it. Thank you for life


Mary says, I know it sounds cliche, but I found my husband when I wasn't looking at all. That's my advice is don't try to force love. We are in a weird transition in our relationship after 21 years of marriage. But if you love one another, it's worth the effort. I'm finding that I'm putting in 110% and he is putting in 25%. So that's the struggle now. He thinks it should be on autopilot. Anyway, I'm going to keep fighting. Interesting. Yeah, I mean, it's definitely one of those things. I think there's a different energy about us when you're looking for somebody versus when you're not looking for somebody. And I've heard a lot of times from a lot of people that they tend to find somebody when they aren't really looking for someone. And I think it's because their energy changes. A lot of times when we're actively looking for somebody, what ends up happening is we start acting and behaving in ways that aren't necessarily attractive because we're taking it so seriously about getting into this relationship. And so the energy that we put out there and that we put out when we're talking to a guy or we're meeting a guy or we see a guy or whatever could end up being really kind of a repelling energy. Whereas when you're not looking, it's like you're more calm, you're more relaxed, you're more confident, you're more comfortable because you're not trying to make something happen and you're not like, okay, I got to get this right and I got to figure this out and all that kind of stuff, which when you're in your head and you're doing that, a lot of times it can end up pushing somebody away because what usually brings somebody in and attracts somebody to you is being playful and being in the state of feeling playful and connecting and having fun as opposed to making things serious and trying to get something to work and trying to make something happen.