Listen, it's the message right here. Black boy, tell me how you really feel. Because I just want to build with you.  I want to keep it real with you. I want to live better, eat better. I want to love better, sleep better. Yeah, I want to feel so aligned. Great Britain is England and Scotland and Wales. That's the Great Britain. That's the Great Britain, OK. So do people identify, so like Scottish people, do they identify as Scottish or British? Scottish. They are Britain. I think they are British. They would thought about it. Actually, yeah. I say I'm British. Right. That's because I'm from England. Exactly. That goes with it. But I think they say it. One of the things that I like asking people is it's an identity question. And I think the reason I ask it is because I'm also in the middle of identities, right? OK.  A woman who happens to be black. Explain. Break that down for me. Because I don't think about the color of my skin. I just feel and how I feel is based on the makeup of me being a woman. So it just comes naturally.  So I can't feel this way. I just feel. So yeah. So let me ask you like this. If there was, OK, let's say a woman just got shot by the police in the past week. And a black man just got shot by the police in the past week. And the feminist schedule in March and the Black Lives Matter people schedule in March or the NAACP schedule in March, which march are you going to?  Like, I don't know. I don't know. I feel like I'm contradicting what I'm saying, but. It's not a simple question. It's not. So. I just feel like I've seen so many black women been killed by the police. Black men killed by the police.  I fit with that community. I would go because I'm tired of it, too. So the black comes first? In that. I don't know. But I thought. But I just know I would. OK. OK. Well, so the conversation, the bigger conversation is about the gap between black men and black women. So the two-part question is, do you think there is a gap? What do you think the gap is if there is a gap? A gap. In what sense? Like a misunderstanding, a separation. I feel like there is. I feel like women nowadays are. Just excelling that rate that maybe men, our age group, are not on. Because you know women mature faster than men. I feel like with a black woman, I feel like black men aren't soft enough sometimes. They can be soft in the beginning. And then over time, it just. It lacks consistency. That's what we struggle with, dealing with black men is a lack of consistency.  And that's what they do. And then he tries to stay because now she's in too deep with you. She wants it to work out.  And then she's like, OK, I don't care anymore. And then because she's not caring, he's thinking like, OK, what are you doing? And why are you not doing what you're supposed to be doing? But really, she's just tired. And that's what's happening with a lot of relationships is women just get tired of feeling like they're saying, do this, do that, and do this, do that. And then when they find somebody else that's doing what they wanted the past guy to do, like the ex starts being toxic and then that's how it goes. That's just how it goes. It's just like. What do you think because something my mom says often is it takes two to tango, right? What do you think women are doing wrong? I think women are. I feel like women maybe like, don't try to think about me. What do I think I'm doing wrong? Because maybe some of us are just too independent. Sometimes because you say you got it, you don't give the guy an effort to be the guy. You know, you might be too pushy. You know, you might be always asking for something. You know, be degrading. Sometimes, you know, men want to feel special. So when you speak to them in a way that could emasculate them, that could be an issue itself. Tone. Sometimes it's not how you. It's not what you say is how you say it. So I think that that may be the few things that I says black women can work on is being softer with black men, too, you know, a lot of men do have my mommy issues. And but if you choose if you choose to be with him, you got to, you know, keep trying to do what he needs. But if it's too much, that's when you just say no, because you don't need to be like being somebody's mom. But, you know, just be soft. So in the beginning, you mentioned how men are inconsistent and they might start off, like, you know, up here. And then eventually they just like, let's I want to explore that. Why do you think that is? Because I agree with you. But I think there's more. So why do you think that is? Why do I feel like they're inconsistent? Yeah. Well, I said, I don't know. They get too comfortable, I guess they, I don't know, lose emotion for the person they're with. I have no idea. I want to know that. Why do men you struggle with consistency? That's what I want to know.   That's all you want to feel is secure, you know. So it's two different types of consistency that I want to talk about. So the first consistency is, you know, doing what you say you're going to do. Now, I think that's just the difference between men and boys, right? When you're a man, when you're a man of respect and principle, you do what you're going to say. So if a man is not doing what he's going to say or what he says he's going to do then, you really don't need to be with that man because he still has growing to do. So that's to one side. Why do men start off going 60 miles an hour trying to impress you and then eventually fall off? That's the more important conversation. This is what happens and I'm going to be completely honest. Oh, interesting. Men have a habit of putting women we don't know on a pedestal. Before we meet you, like let's say we see a girl at the club from far away but we see her in the street from far away. Unfortunately, that is the most beautiful she'll ever be to mostly because I don't know you yet.  And men end up putting all these expectations in their head of her, what she's going to smell like, what she's going to sound like, what she's going to be like in bed, what she's going to be like in the home. She's going to be able to cook. She's going to be the woman of my dreams. The problem with pedestals is the only place you can go after you're on a pedestal is down. So the mistake men make is we prematurely pedestalize women and set them up to disappoint us. So for me, I encourage men to, and that's part of the series, like I encourage men to see women as human beings because if I see you as a human being and I interact with you as a human being you can't necessarily fall off a cliff because I didn't put you there. We're going to interact with each other as human beings as equals, as you know.  It is inconsistent. Like they can't be consistent with that. You know what I'm saying? Like when you look at the rappers who are buying their baby mamas, G-Wags and things like that, you can't do that for 20 years. You can't do that for 10 years, 10 months. You can do it for a week. Yeah, but you can do other things. Sure, sure. You ain't gotta do the exact thing. Sure. That you started doing it, but don't fall all the way off. That don't give you no excuse. What you thought this was gonna be? A two minute situation? No, you got with me. You said you wanted to be with me. You knew the consequences.  But as a man, because you said that you was gonna do this by me, you should have done it. I agree. I agree.  You know what I'm saying?   You don't start being disrespectful in ways that you haven't been to me before, because that's not what you introduced me to. I didn't do anything. So that's just, that's just what happens at the very end of the relationship when it's not being consistent. You just turn me to each other. And in that respect, I do blame men because oftentimes we don't, it's like twofold. We don't vet women properly based off of the right things. And eventually when the haze washes off of our eyes, we end up upset at you, where we should really be upset at ourselves. Like, I didn't pay, I didn't bargain for this, but that's because you just saw titties. You weren't thinking past the titties. And that's what happens. Yeah, exactly. You don't like her. You liked who you thought she was. Yeah. Yep.


Welcome! The following video will demonstrate how to program a GoControl smart wireless light switch into a GC2 security panel. Ensure the panel and device are within 5 feet of each other. If needed, disconnect the panel from AC power and walk the panel over to the location of the switch. The panel will continue to run via the backup battery. Next, navigate to the GC2 Z-Wave Toolbox. Press the Services button. Then press Z-Wave. Press the wrench icon. Enter the installer code. The default installer code for the GC2 is 1, 5, 6, 1. You are now in the Z-Wave Toolbox. The Z-Wave devices may need to have past programming removed before they can be added to a panel, even if they are new. We do this by performing the removal process, which is also known as exclusion. To begin the removal process, select the Remove Devices button. The Remove Z-Wave Devices screen will appear. Press either the top or bottom button once. You will see a notification on screen that the device was removed. Press OK. Press Back. Now you can add the switch. This is also known as inclusion. Press Add Devices. The Add Z-Wave Devices screen will appear. Press either the top or bottom button once. You will see a notification on screen that the panel is querying device information. Do not exit the screen until you see the details for the switch display. Press Back to return to the Z-Wave Toolbox. If you have any remaining Z-Wave devices, you can add them now. Then, install the panel and all Z-Wave devices into their final locations. Lastly, complete a network rediscovery. During network rediscovery, the panel will recreate its Z-Wave routing table and optimize the mesh Z-Wave network. To initiate a network rediscovery, navigate to the Z-Wave Toolbox. Then, press Advanced Toolbox. Press Rediscover Network. The panel will display a message that it is rediscovering the Z-Wave devices. Then the panel will automatically redirect you back to the Advanced Z-Wave Toolbox. Note, the panel will continue to run the network rediscovery process in the background. Network rediscovery will take around one to two minutes per Z-Wave device. Z-Wave services may be unavailable until network rediscovery is complete. Press the Home button on the panel. The Go! Control smart wireless light switch has now been programmed into a GC2 panel.


Welcome to my talk, Fass and Furious. Zero to serverless in 60 seconds anywhere. Now, I want to talk to you about what serverless is, what you can use it for, and also how you can deploy it in 60 seconds. And then we'll move on and look at some cool demos and talk about how some of our users are going on a journey to production with it too. Now, when we talk about serverless, what we're really looking at is a new architectural pattern, a way of designing systems. So, we're all familiar with the monolith. We used to build these three-tier applications. They had a database. They were very slow to test, very hard to deploy. They did far too many things, and we broke those down into microservices. Now, those microservices had more constrained responsibilities, but at the same time, they're actually quite hard to manage, and you look at something like Istio and Open Tracing that have come along to bridge the gap. More functions are the next step in the evolution, and your function is even more specialised. It tends to do one thing and one thing really well. It's a discreet reusable piece of code. You can be deployed and you can pretty much forget about it. So, when we're looking at functions, it's that next step in the evolution. Now, they do not replace your existing microservice and monoliths. In fact, they work best as an integration, as a kind of connected tissue to bring them together. You'll see how you can build an event driven architecture quite easily. An example might be an event from a payment gateway that needs to go into a larger part of your system. I'm actually going to show you a real-life example of that later on. So, this is a cloud-native landscape, and we're all quite familiar with this. We've been seeing it every morning and evening. OpenFaz is actually on here now. I think somebody's head's in the way there. OpenFaz is on there, and I'm really proud to say that this is kind of the chart of all the technology and upcoming projects, and what's kind of hard is knowing how to navigate it. And so, you'll find many of these projects work together to create new value. We make use of Prometheus and some other technologies there, like ContainerD, to add value. I'm just going to specialise on OpenFaz today. I just want to talk first a little bit about something from the industry, from a cloud provider, about AWS. Now, I have an echo dot upon the stage. This is a voice assistant, and it's a great example of serverless applied to an IoT device. You speak to it, your voice is uploaded to the cloud. Probably Monolith will parse that, and then it will invoke a function, a short lift function, and that's it. We'll see it makes use of a weather service. It's probably Yahoo! Weather, another Monolith. The way this works is you upload a zip file, you maybe install dependencies on your local laptop, and then that's it. You upload it, and Amazon will look after the billing for you, but there's some restrictions here, and that model of editing code in a web editor, or throwing a zip file across the wall, is kind of falling short of what we've come to understand as cloud native. Now, you can actually replace Lambda with OpenFaz and Docker, and then you can get that whole ecosystem that we've seen on that landscape and bring that in alongside your existing applications. But why would that matter? Well, you should be able to write functions in whatever programming language suits your business and your team. You should be able to run them for however long is necessary, and that means more than five minutes. And you should be able to run them on whatever hardware you have available. Now, when you come to use confidential data, it's often really important to run the code where the data already lives. With OpenFaz, you can do that today. Now, let's take a quick look at a skill that I built out, and the Wi-Fi is actually being a bit patchy here, so let's keep your fingers crossed. At ADP, I have to go through around 20 clicks to be able to book a day off. Maybe similar story for you guys, and I've built a skill to make that easier. Alexa, ask self-service how many days I've got off. You have nine days remaining. Nine days. Alexa, ask self-service book a day off. There was an error. There was an error. Alexa, ask self-service book a vacation. Sure. Which day would you like? Friday. I will book off Friday right now. Alexa, ask HR how many days I've got. We'll come back to that later. What you can now see is a new interface, and we're able to completely swap out that code and build a conversational UI. Now, you may be thinking what are the use cases for serverless, and this is a very common question. It comes up a lot. Now, when you look at OpenFaz, you can actually make any binary, even a C++ binary from 25 years ago, into a serverless function without changing it in any way. Any binary for Windows or Linux will actually work that way, but I want to talk about some specific use cases and then go into them. So machine learning, you may have seen colorized bot in a closing keynote with Weave yesterday, and we're able to take a black and white photo and turn it to color just by packaging it up as a function. Batch jobs, many of us will be having our payroll processed at the end of the month. Again, that will often be done with a batch job. If you have a function with OpenFaz, you get batching for free and asynchronous invocations. You don't have to build that yourself. Image and video conversion is a very popular workload, and you'll find things like image magic are still being used widely in the industry today. Well, you can take that binary without writing any code and package it as a function. This is a perfect example of a mobile back-end and then chatbots, and I'm going to show you a really exciting collaboration with the Moby Project around that. Now, any function you create directly gets a HTTP API, it gets metrics through Prometheus, so you have observability. There's a little bit about the story so far. I started this project by looking at how to build skills for the Alexa, and I found the experience somewhat lacking. Uploading text, uploading a zip file on a song dependencies locally, that is not the container native way. I was a docker captain, and I thought, let's try to do something. I wrote a POC, it was quite popular, and I figured out that I hadn't done everything right. I heard about the dockercon called Hacks Contest, which was actually in Austin, and you needed to push docker beyond what it had been designed to do. Now, I ended up pivoting the whole project and rewriting in Go, and they accepted it, and this is a bit of a homecoming, so I was actually here on the keynote in May, so it's great to be back here. But since May, I've carried on going, and it's become the top trending project on GitHub overall for about a day. Gained over 8,000 stars, and best cloud computing software from Info World, along with Docker and Kubernetes. Very, just humble by that. There are people taking this to production, and then we have the Kubernetes support, which came in a few months ago, along with Nomad, DCOS, and Cattle. Now, this project would not have the... It would not have this much traction. It would never have grown as big as it has, unless we had a community behind it, and that's a key difference with this project. So, we have over 65 contributors, and I've got some of the highlighted ones here, and top influencers. So, up on the left here, we have... On my left, we have Stefan from Weaveworks, and he came along to try and do a cool demo as a dev advocate, but he actually liked the project so much. He stayed, and he's contributing. Finian with the sunglasses there, you may have seen him in the keynote, he's 17, and he helped me build Colourizebot, and have a story for each one of these guys, including Burton, who's a local here, and we actually got to meet face-to-face. The community is a very important part of this project. We've got over 1,400 commits, and a lot of momentum right now. So, just to go into that a little bit more, we built this Twitterbot, myself, Finian, and Ollie over there, and this is us at DockerCon a couple of months ago, staying up late, helping them add error handling to the bot, making sure that it was productionised ready to go, and this is an example of what it can do. That's just been a great experience. I met Finian in February this year. He'd seen one of my Raspberry Pi clusters, and I advocated for him with Docker and managed to get his expenses paid so that he could come and present at community theatre, and then he came to Copenhagen and spoke again. So I think I'll open ffaz, and personally I just love to invest in people and see them grow. So let's talk about the stack. Now this really comes in one flavour, Cloud Native. The API gateway is written in Golang. That is the central point where all of your functions are defined and all your traffic is routed.  There's a restful API, and every function you deploy will get a route. Prometheus is baked into that, which means you don't have to do that yourself. So as each of your functions are being called, we're actually tracking statistics on that, and we can use them then to call the Swarmil Kubernetes API natively to horizontally scale for demand. Docker's image format, I think we'll all agree, is the way we should be building and shipping software. We don't want to be going back to dropping JavaScript files into a black box. We've learned from that, and the function Watchdog is a component which we stick inside a container as a shim, a sidecar, if you like, in a similar way to Istio, which allows it to become serverless. Your application doesn't actually have to know anything about it. Now this is the function Watchdog. It works a little bit like CGI. It becomes the entry point for your container. And as we get a web request, we'll fork a process and push all of the body in through Standard In. So it's something that every application can work with and read a response through Standard Out. In this example I'm saying Python and Main3, normally you would have had to build an entire Flask application, build your own Prometheus metrics. You get it all for free and that allows it to do some really smart stuff. Now OpenFaz is more than just an open source project, another Faz framework. We have values that we use to kind of drive where the project should go. So this is developer first. It should be unsurprising. We do not want magic and widgets. People come to this project and they say we tried the alternatives and we just didn't know how to debug it when things went wrong. We have a CLI in a UI and they're actually really treasured by the community. Most of our contributions have come around the CLI because it's where the developers come first. It's the first touch point for them. Operational simplicity which means it's very easy to deploy in 60 seconds. It's portable, it can run on ARM, it can run on the cloud. We're even looking at technologies like AKS and ACI by Microsoft to allow you to run OpenFaz very easily in a managed Kubernetes environment. Because it's an open platform the community can come around it and add things that they need. Which has led to developer love. Burton, one of the guys who is local here he actually has been working in his own time to do a POC because he's so into the project and functions and he presents that back to his architects within the team and they're really excited about moving forward with functions. That reminds me of the story of Docker where I did the same thing at ADP. Now this is the rough architectural diagram if you like, 10,000 feet we have the CLI, the UI or any web service coming into the gateway and then you have your functions on the other side. Now the key difference between OpenFaz and other projects is actually you can swap the orchestration framework out. Everything else works exactly the same. And that's what enabled me to make a really tight native integration with Kubernetes in just a few days. Now we've been running that for a few months now. We have support for our back, Helm charts we use services deployments and Kubernetes secrets so there's nothing surprising. It's native integration. So let's look at deploying this. It's going to take 60 seconds and I'm going to show you that just right now. There's two of the demos that you'll see. The first one will be a collaboration with a Moby project and this is where we've built a store that can create you a Linux kit VM and will pick which ones we want and will have several functions that will then talk to Linux kit and a database and eventually deploy to packet.net a live VM. The other demo that I'm going to show you is how we've as a project scaled the community and being able to address some flaws in GitHub's permission system. So let's go into that now. So over here I have a terminal and I'm logged into cluster and you can see there are no pods back into that. Now I just have the YAML here and I'm going to run the YAML in using and I think we've got the consensus it's now called Cube CTL heard it from Kelsey and if we take a look at these pods Cube CTL get pods we're good to go. Now I can show you the open-faz UI and we see we've got no functions yet. Now this is brand new, we actually have a function store and the community is creating this I can click on a function this is the inception function it will tell you what's in a photo and hit deploy that I really like again this is from Weave and this is an SSL checker. So what I'm going to do is I'm not sure if I have automation on my certificate I'm typing in the request body at the bottom I'm going to hit invoke and we've now got a response back from this search checker it's telling me that I've got to the first of the 4th of January to re-register my certificate. Now you can imagine how you could build a pipeline out of this where that is data that's programmatically used and sent to Slack or JiraTicket or something similar. The inception function is what we've packaged with TensorFlow and what we can do is take an image and paste the URL here hit invoke and we get back a categorisation this is 48% C line Now it might not be right the whole time but actually what we've been able to do is take something very complex and in a few lines of code just package it as a function and you can imagine how you could then commoditise this. So let's move on to the demo of the Moby Store Now Abbey in the front here has worked on this with me and what we're going to do is select a couple of containers, is it Redis for instance it's quite a quick one to build and I've added that to the cart now click on the cart and I can see that Redis is listed there up at the top Now the concept of Linux Kit is that you will build multiple containers into an immutable virtual machine and so when you come to upgrade your server you just replace the entire virtual machine there's no upgrades there's no vulnerabilities I'm going to hit build and deploy and I believe this takes about 30 seconds and then over on my packet project 30 seconds we'll see that pop up So let's just go on to the next demo Now does anyone recognise this github repository Do we know what's in there No This is Docker's codebase and the bot that we built for OpenFaz to help our contributors manage the code without actually having full right access is now running on the MobiMobi organisation Now I can take I can create a new issue here test for kubicon and because I'm a Docker captain I've actually been put in a special file that says that I'm allowed to curate these issues and I can say Derek add a label in valid and the bot will pick up that via a webhook he'll then apply the label that you can see there and then I can do things like Derek close Now he also does smarter stuff for our project like checking that people have signed off for their licences so he won't allow us to merge a pull request until they've said yes I agree this can be licensed under MIT and you can see he's closed the issue there and let's take a look at the packet it's actually popped up we can see Root Linux Kit and it has an IP address there as well and that's booting up now on their infrastructure Now the demo that we built, the Mobi Store we see the cart will be empty now is actually a Node.js application and we were able to build this very fast by putting functions together that wrapped Linux Kit and talked to MySQL and that's what that architectural diagram was about Now one tenant of a serverless system is the ability to scale for demand Now this is figlet, it will give you an ASCII logo What I'm actually going to do is I'm going to call into this with Apache Bench and I'm going to try to give a significant amount of traffic and what we should see is because our metrics are being tracked by Prometheus that we'll start to see some data appearing within our graphs and there we go and the function rate is a bit faint it's just shot up here and the replicas are on the right hand side and as that traffic continues to be monitored and observed we'll actually see the function scale up and that's horizontal pod scaling and we've just gone up to five replicas there and now we have more capacity to process that more compute When that finishes it will back off and it will go back to one replica again So you may be wondering how would I actually write a function myself? Well, we have a CLI that can make it really easy for you and we're not going to put you into any boxes you can actually use a full Dockerfile and put the watchdog in manually but we have some templates that make it very easy This says We'll then combine that with a best practices Python template that has an Alpine Linux base a non-rope user and anything else that we think is necessary for your image We'll then combine them and build the image which can be stored in something like harbour, twist slot Docker registry, even key and from that point you have an immutable artifact and this is a key difference between other projects that can be deployed into dev, qa, staging and production and you know you've got exactly the same thing Once you actually start to get a few of these functions you can put them in a YAML file and we generate this for you So here I've got something that will ping a URL and something that will notify me to Slack You can also specify my secrets here So we're using Kubernetes secrets to keep your API token secure We can put constraints here we can set memory limits and then FAS-ULI build, push, deploy That's your workflow So let's talk about how you might invoke a function and you might be surprised by the simplicity of this We have a route for a resize image since we've deployed the function We have our data binary on the client side and then we'll just call the gateway of the URL, posting the binary It will look up the function in the service catalogue with dds post to the watchdog shim and then it will respond immediately synchronously with the TCP connection with your data, that's it It's very simple and this will probably be enough for most people We don't need Kafka, we don't need to involve complex systems However sometimes you may want a deferred execution and this is where you can use an asynchronous way of running your code I talked about batch jobs earlier In fact our colourised bot takes around 8 seconds to run so we don't want a persistent connection we run it in the background So your image comes in to the gateway and it will be queued somewhere Our default implementation uses NAT streaming, which is a very lightweight fast message bus A queue worker will then call the function for you but then how do you get the result back Normally it would just be lost or you'd have to code something to store it somewhere We have a built in mechanism xcallback URL that you could pass with your original call and then when it's finished it will come back to you and that could either be to a function or it could be to an external website or later a great example is to use the inception function in the store and request bin or something with ngrok and you'll be able to get the result back to you as soon as it's ready We also have a Kafka connector that's been built by the community a connector for event grid and we're looking into AWS SNS as well So I just wanted to tell you a little bit about colourised bot because it's kind of great to get the theory but when you put a system together you learn a lot of things very fast We have a tweet listener here Now that's actually a microservice because the API will not allow you to keep connecting you need to have something persistent and that's fine Don't use serverless functions for everything please do not You then have that invoke the gateway but we don't send the image in we actually store it in object storage in Minio Now that black and white image will be there we'll send the URL into the gateway and synchronously invoke colourised because it takes eight seconds you really can't afford to have that transaction open and then once it's invoked it will pull the picture from Minio it will then tweet the picture for you and if we needed to do anything else like notify Slack we could do that we've been able to build a dashboard out of it too because the metrics are just that and you can try this later on the spelling is a little bit odd it's American colour and it's English eyes so one thing that you can do once you start to build functions I think this is where the value comes is cookie cut things so I spoke earlier about a payment gateway right well Dan Cohn in his keynote said that there's this cycle that makes open source projects successful he says you have a project creates a product generates profit that goes back to the project again now we haven't quite figured that out yet but one thing we have done is created a Patreon campaign where people come back to the project $10, $15 and just help with stickers and bits and pieces like that so I created this function that takes a webhook from Patreon every time somebody submits a payment we verify the origin of the message using HMAC which is the same as what GitHub uses and then tweet using the API token that I've stored in a Kubernetes secret and you can take that and you can modify that however you like in fact if you start the open class repository the same sort of code will tweet your avatar to my Twitter feed which is kind of fun as well and you see there Wesley kindly helped us out so this is more than just a POC when it was launched in May there were very grand ideas now we're actually getting much more feedback from people about what it takes to production I'll talk about the University of Calgary we still have around 10 minutes University of Calgary has a HR system and they also have a research management system now when a paper gets approved they need to link that to their HR system they actually have a Kafka cluster there already and already have events that are generated they use serverless functions to connect them together now we didn't have a connector at the time and they just wrote 90 lines of Python code and it just worked and that goes back to what I said earlier about having this open platform I think you'll find the same for yourselves Contiama are a startup out of Berlin and they do a lot of data science now they will take something like a Jupiter notebook and they will build it deploy it and run it as an open class function and give you the results back ADP have been experimenting with machine learning to see if we can detect customer churn and what we were able to do was figure out the Python code of scikit-learn that we needed in a console app and then just wrap it in a function and it just worked it took about 5 minutes so it's very very quick to get that experience to build things up and to start going to production with it now there are a lot of integrations over on the other side of the screen some of these have been done by the community and some are actually done by the project so HashiCore has spent developer resource their developer advocate Nick Jackson has really helped the project and he's created a nomad backend so I said you can swap the orchestration layer HyperSH who I believe are here too created a backend for their cloud which gives you per second billing which is kind of exciting and then Rancher and DCOS came from the community and we have some other integrations that are happening there too now I've put Qualcomm on here because they've actually got a really cool demo with a very big Alexa over on their booth and you can ask it about openfaz and it will show you a slide so it's very cool so go and check that out later so what's next for the project and we have a few minutes for questions too well the function store has actually launched it launched so quick to update this slide so go and check it out there's no more searching through github we said serverless functions made simple and that actually challenged us to deliver on it and so now you can one click and get a really useful function Derrick as a public github app is now installed on MobiMobi and I hope that it will be able to install on other projects too so you can start leveraging the benefits of fine-grained permissions we have a documentation site and there's a little preview of it just ironing that out and then on a technical level the things that are kind of really interesting and also some things that are really helpful so integrating with cloud events SNS queues from AWS you push to S3 you get an event it's currently quite hard to do but I think we can build a connector for it multi-tenancy is something that people ask time and time again how can we create secure isolation between functions well I think some of the technologies that we've seen in the keynote could really make that very easy observability with open tracing again could allow you to build up a very detailed picture of all these functions that you're now managing where are the bottlenecks and then scaling to zero and I think this is largely overcooked people look at serverless and they say well look doesn't lambda scale to zero you should do that too I don't have hundreds of thousands of concurrent users with an over provision cluster I have a team with a payment gateway that needs to run 24-7 and have high availability perhaps it's okay if we have one replica running the whole time it's only 10 meg of RAM right so I think we need to apply some common sense but these are things that we're looking at this is a direction that we're moving in and of course we're open to contributors and I've met some of you here today and had great conversations with you really open on the project whenever anyone wants to join Slack we'll give them a warm welcome we'll introduce you to the community and help you get your first pull request matched whether that's code or just helping in some other way so thank you very much for listening really appreciate it and I think we may have some time for a few questions who's first Abbie can you run Mike thank you sir can the functions can they be developed in other languages is there a way to create a plugin or something that you can use other languages you can write your language you can write your function in any language any binary for Windows or Linux can be made into a function we have templates for about eight common languages that just make it even easier for you a question at the back so OpenFast looks to provide back-end implementations for a number of different providers if I'm looking at this as Kubernetes first and running a service platform on it does OpenFast support RBAC or execution in different namespaces can you repeat the last part of your question so does OpenFast support Kubernetes RBAC for limiting access did you say RBAC yeah we use RBAC and you can choose what namespace to deploy into so you could have two concurrent deployments so I'd deploy two different instances OpenFast then in two namespaces or one instance and it can deploy into multiple namespaces you could do two instances in different namespaces at the moment but if that's something that is interesting to you please get in touch and we can see if there's another way of doing it there was a question up the front as well do you just want to shout it and I can repeat it do you wish yourself yeah is a sample for which one yeah so is a sample for ffmpeg available there are lots of samples available ffmpeg is there we have a community repository called Fasen Furious and that's where a lot of the store images are so yeah you can get hold of it and you can actually get the gif maker you put a YouTube URL in and it will give you a gif from it okay well look I think people are trying to get off to other sessions but if you want to come up to the front and ask me anything else and tomorrow as well my github account is alexlsuuk and openfaz.com thank you


Okay, we're back everybody. This is theCUBE, SiliconANGLE TV's continuous coverage of IBM's information on demand conference. We're here live in Las Vegas. I'm here with my co-host, Jeff Kelly. Jeff, I think this is the 100th time we've been to Las Vegas this year, but. It feels like it. But it's good, this is a good event and we've got a great guest here. We're really excited to have him on. Jim Kobielus is IBM's chief big data evangelist. I added a little something to your title there. I'm a chief. But welcome to theCUBE. Woo! And theCUBE a lot, what's not for you. Great to have you on again. Maybe that's inappropriate, but it may go. All right, we're cool, it's just theCUBE. So welcome back. Thank you very much. Pleasure to be here. And David and Jeff, we're your old buds of minds.  You've been to many IODs, but this is your first as an IBMer. Yeah, that's right. So I'm a different creature. I had been an analyst and now I'm an IBMer who trades on the fact that he's an ex-analyst. Very nice. So this is day two for us, Jim. It's like day six for you, right? No, it's actually day three for me, three of six. Yeah, okay. So you're, Let me keep you busy. Oh, you better believe IBM keeps all of our employees busy as beavers to keep this show running smoothly. So you, as you were saying, you've been to every IOD as an analyst. You got a rich history as an analyst at Forrester, current analysis, et cetera. Talk about why you chose to come to IBM. As an analyst, you could have, you were famous, still are. You could have chosen so many companies. Why did you choose to come to IBM? People ask me this all the time. You know, I could make some, I could have some flip answer like Ka-ching, but that's actually not true. It's actually not true. When he talks at BS walks, people, you heard it here first on theCUBE. But no, that's actually not the reason. I've been in the industry, you know, people know I'm a graveyard. I've been in the industry a long time and I've evolved throughout my career. And there's, I don't really have a career plan. I sort of follow my instinct about what I'd like to do and the kind of organizations I'd like to work with and for and the sort of things I want to do, the skills I want to gain. And the broadest sense, I had been with a solution provider on two occasions very briefly in my career in the past. And I'm not going to name them and everything. But I thought, you know, at a certain point, I'd been an industry analyst for about 12 or 13 years straight. I wanted a change. I wanted a new challenge. And I'm quite familiar with IBM, the good folks from the infosphere, the web sphere, the T's, the SPSS, Cognos. You name it, I was quite familiar with IBM and all of our products from long back. And I'm quite fond of many of our competitors too in the sense that as an analyst, I consulted to many solution providers and I also consulted to their customers.   And what that means is, you're a thought leader, people follow you because you say interesting things.  This is obviously, God, this is an ego thing. But let me just keep on going here.  And do you want to join us and continue to do those same things but as a thought leader within IBM? And I thought long and hard for about five seconds and said yes. So, we were having a conversation earlier. Jeff, as you know, created the industry's first big data market sizing report. And it was fun actually. It was a little bit of tongue in cheek action there. We said, hey, we can do it. Let's just do it. Let's do it. Why not? Our open source philosophy allowed us to get a lot of really great feedback and take some arrows. But that's, that's what we do. But so IBM was one of the leaders but didn't come out on top. And we were sort of debating.  IBM number one. Did we not pay you guys enough under the table? No, I'm just kidding. No pay to play here. These guys have total integrity. Thank you. Just hit edit if you don't believe it. Hit it and contribute. But so here's the reason I'm asking this. So I was reading a Barron's article this weekend about the end of the PC era. And it's essentially saying, you know, Dell and HP are in big trouble and some of these emerging guys, certainly Apple. And then when it got to the big data section, it said big data. And of course EMC is going to do well there.  But my question to you is, is IBM number one in big data and why? Well, big data is such a huge area, a huge market or paradigm. You got to start to break it down to components. And each of which is a huge area.  So is IBM number one? Well, you know who I work for. So let me just, you know, break it down into okay, if you look at what the core of big data is. Big data is essentially focused on analytic databases with scalability, with scalable architectures, radically scalable, clearly, to the petabytes for real time to handle multi-structure data and to, you know, those databases and data persistence or data delivery vehicles are optimized to serve advanced analytic applications, statistical modeling, prediction analysis, blah, blah, blah. Okay, if you look at what the core of big data is, first and foremost, traditionally and very much practically in terms of where enterprises have invested, enterprise data warehouses, massively parallel enterprise data warehouses like IBM, Natesa, like the IBM Smart Analyzer, InfoSphere Smart Analytics System and so forth are the core of the big data market and we have many fine competitors and so forth. But if you look at the three Vs, which is what I'm talking about, the volume velocity and variety, big data has been around for a number of years now as a distinct market segment. The name itself has evolved or become, you know, hip in the last few years. If you look at who are the leaders in enterprise data warehousing, well, look at IBM because we have a rich portfolio that I just mentioned. And so in many ways, I'm not going to cite any work that I did previously in terms of the evaluations I made of various vendors, but IBM is very much the top tier of data warehousing and has been since basically the start of the industry. IBM in terms of the new approaches like Hadoop were one of the very top providers that have had Hadoop solutions for the most scalable requirements. If you look at the announcements, just yesterday here at IOD, we've evolved big insights. We've added additional solution functionality that are targeted at particular industry requirements for Hadoop projects, whether it's telco or finance and whatnot. So IBM continues to deepen our product portfolio in all things to do both with the platforms, the tools, but also the tailored solutions for particular business problems on top of data warehousing infrastructure, on top of Hadoop. And of course we have stream computing with InfoSphere Streams. So if you look at who provides not only data warehousing but stream computing and also best debris statistical and predictive modeling, also best debris BI, best debris data integration and governance with the InfoSphere portfolio, you know, so on and so forth. Who is that? IBM by far has the deepest stack of best debris products across most of the segments of what we generally refer to as big data. So really many ways IBM is number one. Now so thank you for that answer, by the way. I hope you have a lot of time here because we could, I have a lot of topics that I want to cover. I have time for you guys and I talk fast, so try me. Jeff Kelly, I want to put you in the spot. So you just heard Jim talk about IBM's strengths. What do you think? What do you think IBM's biggest challenges are in the big data world? I think the biggest challenges, Jim, are you mentioned the portfolio and it is a wide and deep portfolio, but I think what we're hearing from our community members is a little bit of confusion around the, bringing that portfolio together in solutions that are very easy to kind of translate into business value, talk to the business, help them understand who might not be so interested in whether it's Natesa or Big Insights or whatever it may be under the covers, who wants to know how you're going to solve business problems and put those together in a way that speaks to the business. So I think that's one of the biggest challenges, I think. So what do you say to that, James Kobielus? Well, what James Kobielus says, the IBM Big Data Evangelist says to that is, among other things, IBM is highly, yeah, we recognize that we have to continue to deliver and simplify the solution story of big data across different use cases or outcomes. If you were in the general session this morning, perfect example of how we're continuing to do that is if you followed what my colleague, Faruna, I'm sorry, Faruna, Alarakia, you got her name right, her demonstration of the next best action signature solution that we've rolled out for marketing and customer relationship management, perfect example of how we're delivering through a growing portfolio of solutions, quick value on some fairly complex projects that involve the advanced end-links, involve the big data, involve your marketing platform like a Unica and so forth, but in a way that the way she laid it out was beautiful because she actually laid out how the product works in, let's say, a call center environment or in a marketing environment. In other words, what we're doing at IBM is we're doing the solution cell through increasing bundling and integration and tailoring of a product portfolio that in many ways is general purpose. You can deploy our next best action signature solution across a wide range of use cases in customer-centric channels. So we not only provide these solutions, we provide the business analytics and optimization professional services that can help you in a consultative fashion to assess, first of all, what are you trying to deliver in terms of value in a particular application? What set of tools and components and platforms are key enablers for that? What offerings do we provide already as it were quote-unquote out of the box, I'm going to use that in air quote, that not only contain, incorporate those components, but also bundle in the business content, the predictive models and the business rules and the orchestrations and so forth that are tailored to your specific needs in your industry or your specific need in the business process that you're trying to say automate or make more agile, you know, be it finance, be it marketing or whatnot. And so we work with customers to the whole way in a consultative fashion to make sure that you realize that value and we have domain experts in all these areas I described that we can bring to bear on those kinds of engagements. If the customer wants to go there, we don't force them to use us for professional services. If they have all the expertise in-house, we have solutions. So we can go, we have a wide range of partners we can bring into these kinds of engagements as well to flesh out the overall value story, ultimately focused on the customer. So that's what we're doing, you know, we've been doing for quite a while and I think we do a really good job of probably better than most of our competitors on the solution sale or the solution value proposition from the very start driving the whole engagement. And what about it kind of engaging with smaller customers? I mean, I think certainly, you know, the 4 to 500 enterprise, think of it as IBM customers. What about the SMB, the mid-sized organization that thinks, well, this is really interesting what IBM's doing, but, you know, we can't afford that. That's not in our, we can't do that. It's just not going to work this year, even the next few years in terms of the finances. Well, of course we have a broad range of small to mid-market tailored solution packages across most of our product areas. In fact, we had an announcement yesterday of an SMB focused offering. I urge you to look at the press release on that. I did a blog, in fact, last night. What I'm saying is that we are very attuned to making sure that we have the right solution tailored and priced and licensed appropriately for each market segment from large enterprise to small business. We have cloud offerings for small and mid-market. We have, you know, expert integrated solutions for a wide range of sizes in the Pure Systems product family. So we, you know, at IBM, we want to make sure that we can deliver value to you in a way that won't break your budget and will help you to realize our why on that overall project. So we have, I think we've gotten better in recent years about focusing on the smaller customers who really in many ways drive most economies because most startups, startups by the very nature, start small. And at IBM, we want them to be an IBM customer from the get-go. And so we've got to give them, you know, favorable pricing. We've been talking about going down to Strata this week and totally different conferences, even though a lot of the- It's a great show. I love Strata. I wish they were holding theirs on a different week. I would be there. Yeah, I mean, I'm sure you would. And of course, now it's Strata plus Hadoop world. We should mention that because this was originally Hadoop world and it sort of morphed into Strata and Hadoop world. But the conversation there is similar, but there's a stark difference. And one of the big differences is people will constantly ask the question, what are mainstream businesses doing with big data? Now, you don't hear that question so much here. I mean, you hear it, but a lot of people hear walking around in suits, a lot of business people. And we've predicted, many have predicted, it was pretty easy prediction that the traditional BI, data warehouse worlds, the analytic worlds, the legacy worlds, if you will, and the new Hadoop worlds are going to collide together. And that's clearly what's happening this week at Strata. But I wonder if you could give us your angle on that. I don't think it's so much collision, Dave. I think it's the fact that the new worlds of Hadoop and those sequels and so forth, graph databases and the like, are the nucleus of how, where the industry is evolving into. A great many data warehousing solution portfolios including ours, integrate out of the box now with Hadoop, whether it's Apache or Big Insights. In fact, I'm speaking tomorrow, I'll plug myself. I'm speaking tomorrow on how Hadoop Big Insights can fit into or factor into your data warehousing strategy with, and I'll be discussing particular deployment models for Big Insights in a data warehousing context. That really highlights something that I, was a big theme of mine prior to joining IBM and it remains one of my core themes, which is that Hadoop in many ways, you can see it as the nucleus of the next generation enterprise data warehouse in the cloud. You know, when I use the word enterprise data warehouse I have to do that in air quotes because that term is an old term. You know, now we have cloud analytics, comprehensive cloud analytics, I just blogged on that this morning. So in other words, the terminology itself, let's not let the, or big data as a term, let's not let that get in the way of understanding the new patterns of delivering solutions, analytics and data into new types of applications. And by the way, when I said, Colada, I didn't mean in an antagonistic sort of competitive way, I really meant it in a mashup way. So those two worlds coming together. And that's really what we're seeing. Oh, definitely mashup and interpenetrating. In fact, we have, we IBM, in fact, in our team specifically that I am on, we have people at the strata Hadoop's world this week and because we can't afford to miss it, we're a big enough vendor that we, we can divide and conquer as it were. Most of us are here though. So I'd love to get your perspective on this.  Great, all good there. And then you get the applications and we'd love to see more going on in applications. And it seems to be, you know, getting some traction there. There's really interesting battleground in the middle now. We saw MapR introduced a new platform today. We saw Hadaap last week, you know, Cloudera's got something coming, Hortonworks and Microsoft announced some stuff. And Teradata came up with something. Teradata and Astrodata announced and Continuity Software is announcing there's a lot of action going on in the middle. Can you talk about that a little bit and help us squint through that? In the middle. What do you mean? I want to know what you mean by the middle. So you got the Hadoop layer, right? It's open source. You got the application layer up here. And then in the middle, being the sequel and the NoSQL guys coming together, you got, you know, Cloudera trying to add value. You got Hadaap now bringing in some new infrastructure. You got MapR doing its thing. There's just a lot of innovation going on sort of a land grab going on for that. Like what I'm calling the middle space in between the app layer and the core Hadoop infrastructure, HDFS. Yeah, you know, I would interpret the word middle in this context to refer to the tools and the prepackaged models and algorithms and libraries for MapReduce and R and so forth. And the vendors who specialize in providing tools for modeling and managing and optimizing and doing governance on big data and doing search and all that stuff. Protecting it is another big thing. Yeah, the whole middle needs to be continued. What we've made and continue to make deep investments in the quote unquote middle in IBM research. And, you know, for example, big insights continues to evolve. In fact, we have announcements this week on new functionality there due to our deep investment ongoing in building out all of those tools and making it all work together more seamlessly. Not only within, you know, the Hadoop universe, but Hadoop is just making it work together seamlessly with your data warehouse with your existing data integration or ETL tools and the like. So in terms of the middle, you know, there's a huge and very interesting space of vendors, many of whom are IBM partners. For example, KarmaSphere on the modeling side. You have a number of others like revolution analytics on the advanced analytics with R, so forth. At IBM, we partner with a great many of them. We ourselves are doing investments in all those areas. We are evolving our portfolio, especially the big insights, but also InfoSphere streams, to work out of the box with the middle layer of tools. Which is the layer of tools and infrastructure and services you need to make big data more robust, more scalable, more manageable, easier to do root cause analysis, easier to ingest the data. Like data click, for example, we announced that the other day, you know, more user friendly. So you don't need to have to be out, you don't need necessarily to hire expensive talent off the street who've got some grounding in MapReduce that it all just sort of works out of the proverbial box. That's what I'm describing there is really, in many ways, the focus of our ongoing product development and partnering in this whole evolving arena. We're getting a hook, but I have to ask you the one last question around the whole database space. Three, four, five years ago, you'd go to a party and somebody said, I'm in the database business, and you go, oh, I'll pour you. And now the database business is like the hottest thing going. It used to be boring. It used to be boring. And Sybase is now a mobile play for SAP. You got Oracle and IBM doing the urinary Olympics around who was first with multi-tenant databases.  I love that. Because IBM was first with a multi-tenant database, folks. Oracle did not invent a multi-tenant database for the record, but now you've got a spate of NoSQL guys coming out and some unbelievable innovations there. What's your angle on what's going on in the database business? And then we'll break. Yeah, I'll try to keep this really brief. This is the golden age of database innovation. You know, all the NoSQL by itself is a humongous catch-all for key value stores and graph databases and columnar and memory and whatnot. That is hugely hot. And clearly not only are we monitoring and doing investments and we're continuing to do, as it were, surveillance of that space. Because IBM makes strategic acquisitions. We recognize that there's probably some areas there that we're not the innovators on. That, you know, we bought Natesa. Natesa was an innovator in data warehousing appliances. We reserve the option of, you know, pulling out our checkbook when it makes sense strategically for us. It's a hot space. IBM writes checks and code, folks. But not physical checks. I don't know. Maybe Ginny Rometti does, but I do. All right, Jim Kobielus, thanks very much for coming on theCUBE. I wish we had more time. It was a fantastic segment. All right, keep it right there. We'll be back with our next guest live from IOD in Las Vegas. Keep it right there.


We're going to get started because we're the last session of the day, and somehow we got in a 30-minute slot with about 86 minutes of content. So we're just going to go fast and furious and get through this. So I'm Bill Aske, this is Jay Brodera. We're from McMaster University in Hamilton, Ontario. We're going to be talking about our infamous Vivo and Elements Implementation Project that's about research information management or research intelligence, as Tom Kramer suggested earlier. So just to give you a little bit of a sense of what we're going to be talking about, we're just going to give you a broad sketch of our context for a couple of minutes. Then we're going to talk about the requirements and the needs of what we're trying to accomplish, go through the implementation, talk fairly extensively because this is really the meat of the presentation about the challenges and solutions we came up with, and then sort of a quick roadmap of what's next in our project. And then some recommendations for, I think this is probably why some people are here, is they're looking at projects like this. Some recommendations and takeaways for you to think about. Okay, so we thought it relevant to give you just a brief contextual background of McMaster. So McMaster is classified as a medical, doctoral, research intensive university in Canada located in Hamilton, Ontario. In total, about 30,000 full-time students, undergraduate and graduate students. About Patriot McMaster lists 949 full-time faculty, keep that in mind, and also quotes itself as the most research intensive in Canada $405,000, Canadian dollars, note, annual funding per faculty. As well, we thought it to be relevant to give you to find the acronyms and the Canadianisms upfront so that when we mentioned these later on, you don't have to take time to think about what they actually mean. So first of all, talk about systems. So a RIMS, also referred to as a CRIS or a RIS and many different things, research information management system, a system capable of integrating information about research researchers and their scholarly activities. Elements itself is a specific RIMS developed by Simplectic, which is part of Digital Science Suite of Products. Vivo is an open source link data web application and it's an ontology for representing scholarship. And then in terms of people and groups, VPR, that is our Vice President of Research. RHPCS is the Research and High Performance Computing Services, so a unit within the VPR's office that we worked with on this project. And we want to distinguish the two types of faculty that we'll use here interchangeably. So in Canada, faculty with a capital F means colleges, means the primary academic units in the university.  So we had a challenge that I think a lot of universities face, which is that a lot of different websites from the various capital F faculties present profile information per the style and the dictates of that individual faculty. And so you have a lot of information, but it's on various subdomains and there's definitely no central store. So from a web indexing standpoint, that's kind of a disaster. There's fairly inconsistent profile content, so different faculties would highlight different information. The ones in the humanities are incredibly text intensive. They give very long overviews of their research, but they're a little bit light on the data, whereas in engineering, they have all the publications. It's just very, very much a hill and valley type of profile situation. Beyond that, there are various designs. I just grabbed three screenshots just showing that everyone uses different templates. It's complete web chaos. And these are, at their core, technically speaking, simple CMS web profiles. There's no automation in terms of how the data gets fed into those. These are all just bespoke, handcrafted web pages. And they don't publish to the web as linked data or anything fancy like that. So when you have that kind of situation, you have a serious problem with currency. Every time one of the faculty updates a webpage that for a while they have these wonderful, and a lot of them just did this, they have these wonderful, great-looking web pages. But of course, they age very quickly because there's no one that's going to go in every two weeks and out all the new publications because it's all manual labor. The consistency is clearly a problem. Accuracy is simply a problem because what they use is their data source for publications or other information is often highly suspect or at least not incomplete. It requires, of course, manual labor. Someone has to do this work and you can imagine that the faculty aren't super keen on yet another place to curate their information. And you have repeated entries. So they have to put the information here. It has to go into their faculty reporting systems and so on and so forth. Beyond that, McMaster, like a lot of universities, but others are a little bit more fortunate that they have more robust information systems. Tom was describing what Stanford has earlier.  There are various systems across campus, but they don't talk to each other. And so there's a lot of data silos. And silos, of course, will be a feature of this talk like there are of so many. There's actually zero interoperability between the silos. And this distinguishes us, for example, from Tom's talk earlier. McMaster really has no IDM solution in place. And so we have a hard time just knowing who is faculty and anything about them. So that was also a major challenge for our project. So in terms of what we did was we, the idea began percolating in 2015 and in 2016, we put together with RHPCS a proposal to the provost to do something about this. And so we went after $150,000 to set up what we said we were going to do. We set up a vivo instance with attractive, consistent, accurate profiles, at least stubbed shell profiles for all faculty at McMaster. And that we would bring in some publications from external sources. And that we would sort of try to persuade faculty that there's value in doing this because they would have a public profile that would be consistent and so forth. Of course, I don't want to say what happened. I don't want to get ahead here. So we got 148,000 out of 150 and we used that money to hire a project manager to do a little bit of hardware purchasing. We said we were going to buy out staff time from RHPCS in the library. In fact, we just ate the cost which is I think a key takeaway here is that we just put an enormous amount of human labor into this without compensation. And we hired it in July of 2016, we hired a project manager and we began sort of actually doing the implementation work. I think that's you. Okay, and then so we've kind of parceled us out into six month chunks after this point anyway. So for the four months after that, the first few months were spent really just scoping gathering requirements, scanning for and classifying various internal, external data sources that were available. So there was a lot of discussion with various groups and figuring out who had what data and how current was it and how complete was it. And the outcome of all this work, two major outcomes here was number one that there was no internal data source that could be considered comprehensive and complete for all scholarly works at McMaster and that building custom applications to harvest researchers publications from the accessible external data sources was beyond the amount of resources in the time that we had. And so that led us then to investigate a solution that would help gather that information and feed it into Vivo on the front end. And so over the next six months, October, 2016 to April of 2017, we explored symplectic elements as a potential solution for that. And during that time, we continued to have conversations with various groups around campus to understand, you know, to their interest in this and their potential needs or requirements that they could get from this. And there wasn't a whole lot of enthusiasm, to be honest, about this product. Many groups were reluctant. They were locked into what they were doing. Some were pessimistic. We tried something like that and it didn't work. So I don't think it'll work either for you. So in April, 2017, we signed a contract for symplectic elements to be the rims as well as the Vivo connector. So they do the work of connecting the information in the rims database to Vivo as like data. And so in the end, this is essentially what the system looks like now. And so between April and October of 2017, we have a rims for the entire institution. And so essentially how it works is that we get data extracted from a central HR data warehouse. We do some automated cleaning and formatting for ingestion into the rims. We also take in non-faculty, we allow them to sign up via an online form and we can ingest them at the same time so they can have profiles or they can just have accounts inside there as well. So once a faculty member has their search parameters refined, then the rims does the work of automatic publication harvesting and tries to do some of the deduplication disambiguation work. Of course, there's still work required on the researcher's end in some cases to finish up that publication claiming to actually claim what's theirs and what's not theirs and also enter their other relevant scholarly activities. And this can be done by the researcher themselves or any delegates that they allow. So the information then from the rims is pushed out to the Vivo site at the front end, which we've branded McMaster experts. And the data from the rims is also available to other internal users via the API. So for uses in internal reporting, web page content population. So getting back to what Dale was talking about, they can integrate this and make sure that they have better currency with their information. So just a quick show of what the profile pages look like so that the elements pages are private to the individual or their delegates. And the main profile page shows a research summary, just the typical information about research output. And there's a carousel that prompts the user if there's actions required, such as entering an Orchid ID or claiming some publications. So in edit mode, the researcher can also add in other relevant biographical information, educational details and assign themselves a research areas, just something we'll talk about in a little bit. And the publication manager page, users can feel their publications claim or reject what has been automatically harvested from their system or manually add publications by entering all the metadata themselves or entering a DOI and hoping that it's somewhere in Crossref where they can pull it back most of the metadata. So McMaster experts, the Vivo site looks like this. And you can get there at experts.mcmaster.ca. So this is the main landing page and it looks more or less like a Vivo front page with a little bit of skinning on it. We also have an area that is populated with tag media stories. So the communications public affairs department, tags or stories and they get fed automatically to the box in the front page. For an individual, their profile page will show name, title, contact information, a research overview if they provided it, a LinkedIn institutional affiliations and appointments and those come from the HR feed as well as the Sparkline showing cumulative publications over the last 10 years. And then there's the stock Vivo visualization such as the researcher's co-author network. And one addition that we've made here is that profile pages are also integrated with media stories. So when individuals in communications public affairs tag this individual in their stories, then those stories are harvested and then fed at the bottom of the profile page. So it's tied into our branding operations. So and finally, from October 2017 of past year, sorry, to present, really what we've been focusing on is wrapping up this project and transitioning to a sustainment mode. This is mostly involved hiring two full-time library employees ongoing full-time to sustain operations and developing the appropriate governance structure so that we can ensure that there are feedback mechanisms between the users of the system, those who operate the system and those who provide the governance with that as well. So as you can imagine, doing something like this in an environment such as ours or yours perhaps, brings a number of challenges and solutions. And I think the first one would be just to point out that we were building this. This is of course the obligatory cat herding slide. We were trying to build the system without any sort of strict requirements from a defined audience. We know that universities need this infrastructure. They just don't know it quite yet. So you have to build it to convince them that they need it and then they of course want it. So we just saw a need and so we were sort of whipping people into shape. And the funny thing is this is actually a poor picture because it will imply that we were on the horses and we were herding the cats. When in fact, a better picture would be we'd all be cats running around just kind of spastically because we were cats ourselves and had to sort of learn the hard way how to get on the horse. And that was perhaps kind of the most difficult thing. The other piece was that and of course you have to have a picture of silos but our silos are as you can see shiny and silver and they're connected by infrastructure. There's infrastructure connecting them so that something in one silo can travel to another and travel to a central silo. So our silos are an improved version of silos. So the faculties are building all these websites with no integration and now there's the possibility anyway that on an ongoing basis with whatever CMS they're using whether it's Drupal or Plone or whatever they happen to be using they should be able to pull in at least the publication data from the elements database. And that would be a huge way of sort of addressing the currency and manual labor issues of maintaining those profiles on their website. So we've at least created that possibility. That's sort of the superstructure here. Another challenge we ran into was that there were no Canadian or certainly nothing on campus, no ontologies to describe research areas. So there are various ontologies one could pick and use but they're always sort of partial and incomplete. So we ended up using the Australian New Zealand I was with the rest of the SRC, yeah. Which is quite robust and fairly large but not overly large and complex to use within our Vivo instance and it's within our elements instance. And it's worked fairly well for us. And last but not least, the work that we're doing is sometimes perceived by others on campus in JLUB to this as possibly threatening their jobs or their livelihood because we're competing with other systems that have been built on campus that have not been able to sort of solve the problem but they definitely see this as an intrusion upon their turf. All right, so one of the challenges that we encountered that we didn't expect to encounter was a complete lack of data governance oversight and foresight at our institution. So we went searching for, yeah, I don't, we should have really expected that but we didn't think it'd be that bad. Maybe I'll put it that way. So what we discovered was there was no authoritative vocabulary for things like department, faculty names, position titles. And we also learned that the HR files are populated, started, and may be maintained for purposes of finances. And so as long as people get paid, then there's no real other issue with the data is what we learned. And so most of the information that we were able to access had few to no standard practices used and so we had to do quite a bit of work to clean that up. And in fact, we did so much work that we became the de facto authoritative source administrative information on campus with other projects contacting us, asking us if we could give them a list of all faculty members on campus or everyone's appointment on campus, group memberships, et cetera, et cetera. We even had HR contact us to ask for clarity on department names at one point to which we said, oh, I just use Google and I'm not really too sure either. So the good news is that this has led to an ongoing partnership with campus IT and HR and they really now, they get it, the importance of data governance and they're making great strides to improve that which improves us as well. And finally, thinking about linking within and beyond the institution. So Dale really talked about research areas already. I think that's the most salient point where we see this need. What we would like as a Canadian set of vocabulary research areas and something that we can take to faculty members and say here's the upon high tri-agency's standard classification of research areas because what we've discovered at each faculty likes to describe themselves using research areas that are their own research areas and by their own, I mean their own, they create them themselves. And it's just push and pull about giving faculty members, giving faculties the ability to describe themselves but then making sure that you're not reproducing the silos that we've been trying to break down over the past decade or so. So another challenge we've had, and boy, our screen's gone yellow. I swear these pages are actually white background but is that we've, you have to persuade faculty at some level to buy in. They do have a curation role to play in elements. It's not completely, you can't completely automate the system. They really want to have an accurate and fulsome profile. They have to do some curation or delegate someone to do that for them. So this is an example of a sort of an empty profile. It's a lovely outline of Donald Smith with absolutely no information about Donald Smith other than his name and email address. Not terribly information rich. So that's a bit of a challenge. The other is that multiple points of entry. We've heard this in other talks today. I think that it's more work for faculty. They don't see an immediate benefit from this and so it's very difficult to persuade them that this curation will somehow yield results for them. There's minimal staff support for that type of work. They don't universally across the campus have someone that they can go to and say, would you do this on my behalf? Some faculty have that, some don't. And it also triggers, asking people to do curation, it does trigger scope creep. As soon as you ask people to do something, they want something back. They want something for their labor and so they start asking, can I do this, can I do that? And can you turn this on? Can you turn that on? And it's like, well, yes and no. Let's talk about that some more. So you sort of, the harder you push for curation, the harder you get resistance in terms of scope creep. There are some perhaps high and unrealistic expectations on campus of what we'll call a stub rims can provide. We're running elements in a fairly modest implementation without a lot of the modules that it offers. And so the people are coming to us and asking for certain types of things so that they can do analysis and there's lots of interest in that. But the current elements implementation is light years from that. They also don't understand that part of what they want to do is use data that's not in there yet because they haven't curated it or they haven't actually created completed profiles. So when deans come and ask us for business intelligence or research intelligence, we basically tell them, well, we can give you something, but it's incomplete. So have you talked to your faculty about doing this? We really want to drive this website integration. We want them to stop doing all this hand labor. We want to sort of put a break, the breaks on all of these bespoke ontologies that rise up. And the one that I'll put out there is and I think we're not the only institution is we have a bit of a health sciences gorilla, which is one faculty that is larger than all of the rest of them put together and has a lot of really interesting and unique attributes. And Jay said earlier, we have 949 faculty, but if you go to our experts instance and you click on faculty, we'll say we have 5,640, okay? Now McMaster calculates for the purposes of ranking and the purposes of its rankings inside Canada takes the amount of funded research, divides it by 949 and says, look, we do 400 and change per faculty member. If you divide that same number by 5,640, you can imagine you get a slightly different integer. And so anyone with any basic facility in math can look at this and just sort of poke it when you've got a six sex order of magnitude difference between the number of faculty you say you have and the number of faculty that you claim as faculty. And so this is an open and unresolved issue that is of quite, it's quite an entrenched issue between health sciences and us and other people at the university around, who's in elements? We think that there are probably of those 5,600, a good 3,500 or more will never, if ever, even know they have a profile in the system. So they're always going to just remain empty sort of stub profiles. And so therefore sort of degrade the utility of the interface. So there's in this basically, to sum it up, it's just a classic card hoard situation. You know, we've pushed the university a little bit further down the road of doing research information management, but we have not built the system that can do a lot of the things that people want it to do in terms of providing actionable, useful data for the university. So let's wrap this up so we can get to cocktails. Real quickly on future directions, we're going to take a whole bunch of stuff people are asking for, this card before the horse stuff.  And it's like, yes, we could only if you give us money. So we want to sort of take all of that and mash it through a big filter and turn it into actual actions that we can take afford and sustain. And that's, of course, a very limited list. So that's sort of our biggest challenge right now. All right, and then a few final thoughts and then we'll open it up maybe for a little bit of discussion, hopefully. So, I mean, to wrap it up, we would consider this as a success. I think we did everything we set out to do and a lot more than what we set out to do, too. And so can you. This is the part where we say, you could also do this, but we also say, you might not want to do this, though. I mean, the challenges are considerable and the obstacles are considerable and oftentimes it can feel very thankless, at least in our situation it was. I mean, it feels good to do it, but some days it's just, I don't know if it's worth the, if it's worth the headache. I've aged a lot. Also, it's mostly about the data. So, Vivo does everything we thought it would do and we're very grateful for that. But what we've discovered through this process is that the value, the value at this point anyways, is with that data as well, centralizing that data and having some consistent product that you can use and that others can use as well. And finally, Tom talked this morning about rims not being a profile page. And we'd like to talk that, I'd like to say that Vivo is not a rims and this is something that we learned yet, of course, because we've heard grumbling so that it might be. At some point, so I think we have a few minutes to be happy to open it up for any questions or comments. Well, we're over time and I really appreciate you coming last to the day and staying over time. So, thank you very much. Thank you very much for the last couple of hours. Right, you were about to say the same thing.


What's up everybody, I'm TheMangus, you were awesome and Predecessor has made some changes since the first alpha weekend. It's very nice to see both Strange Matter and Omata so quick on the draw when it comes to patching in updates through their respective games. Today we're going to talk through these updates, point out some remaining bugs, and have a good time with a few highlights. If you enjoyed the content, please leave a like and subscribe if you want to see more. The first update we had was that they are now allowing you to duo queue. This is a nice compromise that lets you play with a friend while still not allowing coordinated 5 stacks to ruin the experience for people. I did have one game where I duo-queued with Gathisen and we had our friend Zella queue up at the same time, and we all luckily ended up on the same team so it is possible to somewhat cheat the system, however it was just as likely that Zella would have ended up on the opposing team. They also updated the menu screen to allow you to look through the items before entering the match which isn't quite nice. As far as gameplay, they added a rotation to the river buff so now you can get ability damage, damage over time from landing basics, movement speed, or a shield kinda like the old green buff. They also added the red and blue buff back into the jungle with the red buff allowing your basic attacks to apply a slow anti-damage over time and the blue buff giving cooldown reduction and mana on hit with basic attacks or abilities. They added a new active item called Flame that acts as a smite to jungle or lane minions.  They are also taking a hard stance on draft dodgers, applying penalties to those who dodge and yes both in fault and predecessor you have dickheads dodging because first pick took their hero. Ameda is taking it as far as revoking keys for repeat offenders or people that are just continuously toxic in the game. I am all for this, it's an alpha and we are supposed to be helping them improve the game. If you are fortunate enough to be able to test it then stop dodging, place something you are unfamiliar with and move on. As far as bugs go I didn't encounter many other than the minions attacking me sometimes for like no reason but you also encounter random indicator circles left over from gadget mines. Even when I know it's not really there just seeing this circle makes me veer around it sometimes because gadget mines freaking hurt. I did see some people reporting that Kai's Q wasn't working as intended but I didn't have any problem with that. They also had some problems with the servers whenever they first started up.  That was fixed within like an hour. Ameda is going strong with predecessor, the core gameplay is extremely smooth and polished, they're adding stuff in as they go building upon earlier iterations and then testing the effects of the new buffs and items. I really like the slow and steady approach, they're making sure that the foundation is solid before building upon it. Everyone who has had the opportunity to play seems to be having fun with it and they're continuously rolling out more and more keys so make sure you head to their website and sign up, keep an eye out for your favorite streamers and youtubers for giveaways and stay active in their discord for a chance to play. Going to roll some highlights as I go but for now this is the mangoo signing off, you guys have a good one. A mangoo!


What's up, everyone? It's your girl, Kenzie here from Layton Sports Cards, and I'm ripping 21-22, Top 2 A for Champions League, Chrome Soccer Hobby, 4-Box Break No. 9, Random Teams. Again, here's a look at the random results. You can always find these at Layton Sports Cards at any time. The link's also in chat for you. Here we go. Good luck. All right, Big Lit, Big Hitlerkin here. Come on. Let's see some crazy. Let's see some crazy. Only spots remaining for daytime breaks is going to be mosaic. Basketball, pick your team style, and every single team left is on sale. Every single one. So, let's get that filled up. We've got three of those going today to end the daytime, and then fours will be live tonight for some more group breaks starting at five. Some more baseball, basketball, and football. We've got Alexander Arnold Refractor for Liverpool. Yeah, it's definitely worth a try, Daniel. You never know, man. Definitely get lucky. Refractor of Gutierrez for Dortmund, for Russia Dort. She don't know. Hazard. We've got Gold Otto of Joe Gomez for Liverpool, 25 out of 50. Marvin N with Liverpool. Beautiful gold. It's a nice looking Otto too. Congrats on that one, Marvin. Where's Ramirez? I love gold. Just for you, Ramirez. Who needs a sound bite? On Sufati Refractor. Yeah, there's the gold. Galazzo of Zabosli. Refractor rookie of Phil for Sheriff. What's up, William? Doing good. Thanks for asking. Refractor Moriba for Leipzig. Rookie. Let's go. We've got Rookie Refractor, Chasing Glory of Luka Susage. Salzburg. Kamavinga, Future Stars Refractor. Galazzo of Phil Foden. We've got Purple and Gold Zabosli for Leipzig. Half here. See some more gold. We've got Rookie Speckle of Consale for Porto. Felix Purple, Mini Diamond Rookie of Sebastian Thill for Sheriff. 227 out of 250. We've got Future Stars of Gravenbridge. Future Stars Anthony, I ask. Pink, Mini Diamond of Pepe for FC Porto, 112 out of 175. Green Wave Rookie of Astor, Vansik, Wolfsburg. 93 out of 99. Sick color match there with that jersey. Second card. Mendes Refractor of Musiala, Fire Munich and Last Back of the First Box. Silva and Anthony on the Speckle for IX. All right. Here we go. Box two. Kunde, Future Stars Refractor. Galazzo, Finney Jr. for Real Madrid. Here, Allison Becker, Liverpool, Alisson. Refractor, Weston McKinney, Free Aventus. Rookie Refractor, Charlie Savage, Manly U. Speckle, Rookie of James McAtee, Man City. Got Purple, Mini Diamond Rookie, Liam Dillat for Man City, 105 to 250. Mini Diamond Luca Modric for Real Madrid, 27 of 275. Hope everyone's having a great Thursday. Remember, y'all, we've got our 400 Box Super Bowl mixer on Saturday. Chase and Gloria Mbappe. Nice one there, PSG. Lil' and Baby. 400 Box mixer. Super excited for that. Cole Palmer, Rookie Future Stars. Yeah, really. Have it on in the background. We've got a Balotore on the AC Milan Rookie. Uh, Aqua. Sorry, Lava. There's Lava there. Refractor, Henderson for Liverpool. Ellie, Refractor, Ruben Diaz, Man City. Four teams left on the first Mosaic basketball. Let's get that filled up. We've got Chase and Gloria Insert Auto of Larturo Martinez for FC Milano. Milano and, let me find it. There's a Insert Refractor Auto. That's sick. I've not seen too many of those. 96 out of 100. Yeah, Inter Milan, sorry. Did I say Milano? Inter Milan. That is Mark S. There you go, Mark. Mark, staying hot. Refractor, Rookie of L'Lahagi for Los Calili. Little Cup Champ and Messi. There you go. We've got Speckle of Cavani for Manu. We've got Rose Gold Mini Diamond, Van Dyke for Liverpool. One of my favorite parallels in this. 124, 125. Love that Rose Gold. Last Pack, Box 2. Ketelarri on the Future Stars. Refractor for Club Rouge. Box 3, Box 3. Xavier Simmons Refractor, Future Stars. Galasso of Lukaku, Chelsea. Don't like him right now. John Stone's Refractor, Man City. Moreno Refractor. Refractor, Vanneken. We've got Aqua Wave of Ivan Perisic for Milano. Nice one there. Inter Milan, 147 of 199. Inter of Zabosli. Tim Way, Los Calili. Speckle. Got Blue Rookie Wave of Samuel Edozy for Man City. Nice Blue Wave Rookie. That's 38 of 75. So Man City. David M. Chasing Glory Refractor of Kuna. Future Stars at Yemi Refractor. Cantona Galasso. Nice one there, Man U. And Eric Garcia, Barcelona on the Gold and Purple Pinarello. Refractor of Tadich. Refractor, Rookie Auto. Daniel Braganza for Sporting Club de Portugal. That is Kristoff S. On the Base Rookie Refractor Auto. Desk Refractor, Barcelona. Speckle of Pino. Really real. Got Rookie Refractor of Cristiano for FC Sheriff. 182 of 225. Sheriff. Last Pack, Box 3. Pedri on the Future Star Refractor. All right, here we go. Box number four. Box four. Again, up next, we've got a Bowman Draft Super Jumbo Box that is filled up. Then after that, we're going to get into the Mosaic. Again, there was only a couple teams left in the first one. All the teams that are remaining are on sale. Four box breaks there. Looking for the case hits. Translucence and stained glass. Of course, all the different beautiful colors that come out of Mosaic. Two Moococo Future Stars Refractor. Brendan Aaronson Refractor, Salzburg. Ronaldo base. We have here Pedri on the Gold Lava for Barcelona. Wow. That is nasty. Barcelona. That is Chia L. That's so sick. Beautiful Pedriato. Love that. Very nice. Beautiful hit there. I'm surprised those aren't numbered. That's so nice. Mbappe. We've got Bebola, Juventus Refractor. We've got Imcheca, Wolfsburg, Speckle. Chison Glory Refractor, Erling Holland. Got Tenali Future Stars Refractor. Galazzo, Holland. And we've got Agave Rookie on the Purple and Gold. Barcelona heating up here. Nice. Still a nice card though. Cherokee Refractor of Edozi Man City. We've got Marquinhos PSG Refractor. Harvey Elliott Future Stars Refractor. Galazzo, Rookie, Gabi. Barcelona. It's a Barcelona box man. Refractor of Benzema Real Madrid. And we've got Neymar PSG 73 of 225. PSG this time is Wenchu L. Nice one there Wenchu. The Neymar. Got Aqua Wave of De Jong for Barcelona. 69 of 199. Refractor, Harry Maguire, Manu. We've got Speckle of Stéphane de Breche. Milano. Last pack here. And we'll do a quick recap. Refractor of Del Piro, pre-eventus. Alrighty, one second. And do our recap. Our numbered cards. We had the Vrancik for Wolfsburg, Edozi Man City. Our autos Daniel Breganza for Portugal on the Rookie Refractor. The Pedri on the Gold Lava. Not numbered on that one. Beautiful hit there Barcelona. The Chasing Gloria Martinez for Inter Milan. And the Gold Joe Gomez autograph for Liverpool. That one was 25 out of 50. That will do it for soccer today everyone. You got Bowman Draft Super Jumbo coming right out. Thank you.


Welcome to Mahabalipuram, a coastal town full of delight where history, art and natural beauty unite. Explore the sacred ground where spirituality and divinity are closely bound. Stroll along the sandy shore where sunsets leave you wanting for more. Wander through the panchurathas, monuments of might, carved in stone, a UNESCO World Heritage Site. Let the rock-cut caves transport you in time where legends and myths intertwine. Immerse yourself in culture art where music and dance captivate every heart. So visit Mahabalipuram, let its magic unfold where the ancient stories never grow old. Take in the richness of its hue, let the spirit leave a lasting impression on you. Welcome delegates to Mahabalipuram.