I'm John Furrier, the founder of SiliconANGLE.com, SiliconANGLE.tv. This is theCUBE. Our telecast will go out to the events and we extract a signal from the noise and we share that with you and I'm joined with my co-host. I'm Dave Vellante of Wikibon.org and we're here with Michael King of Data Direct Networks and John, we know DDN, a company with a heritage and high performance computing and Michael, you're in the cloud area and you focus on the media and content business and John, as we were just talking about, it's a different animal, isn't it? Yeah, well DDN is a company that's been around for a while and what I'm really impressed about DDN is, is that one, they have some good technology but they're self-funded startup. They're doing very, very well financially and they're growing like crazy and recently poached away from HP, Jean-Luc Chatlain, who really was heading up HP's information governance program at HP and was doing an amazing job and then he came to DDN and he's heading up the strategy and the technology strategy over there and since has been really putting out some pretty good products, one of them we evaluated called the WAAS Web Object Scalar. So you guys have a huge booth here, great presence, great location, it's a packed booth. So tell us what's going on at the booth and what's the buzz at DDN and NAB? Yeah, well so, you know, I listened to your intro just a few minutes ago there and all of those things you talked about are fitting right into our sweet spot, right? The fact that these cameras are all getting higher resolution, right? You talked about 3D kind of going away but the thing we love about 3D is it doubles all the data storage, right? So when you look at it from a data storage perspective, 3D things like James Cameron doing 48 frames per second, that kind of stuff, it's fantastic in this industry for us in terms of building up big data, right? Big data and media entertainment. On the show floor, we've got several demonstrations going on, we've got a unified workflow which shows all of the creative suites from Adobe, Apple and Avid all working together nicely as much controversy's going on in that industry at the moment, it's fantastic to have a solution that allows those people to make some choices possibly about their future. We've got some great things in terms of flawless performance going on for the broadcast world and then we've got our cloud collaboration going on as you mentioned a moment ago. Let me ask you a question about the market here. Honestly, on the film side, we're talking about the academies of arts and quote sciences. There's a lot of science and the film guys are totally into the science side of it. Broadcasters, they do just enough to look good on camera but their back office ID is less than stellar over the years when we're talking about tape. That has to change. So what are you guys seeing in the trends both on the high end, studio side and also on the broadcast side around how they're dealing with their data? What are their requirements and what are some of the things that's changing how they do their business? You know, the thing that's interesting about the broadcaster side of the world right is the flux to HD obviously was huge for us as well. Seven X increase in the amount of storage that people are doing, right? But it's really about, exactly. It's really about the quality of service. So you think about it, you talked a lot about technology a few minutes ago and keep in mind everybody, I'm from the marketing department so apologize when I say some of these things but it's really not about the technology for us. It's about the people being able to work elbow deep in technology but really still feel like an artist, a broadcaster or a journalist. So we try to take that level away from the storage and allow it to be easily usable and that's true in the broadcast space as well as film. Now I've noticed Michael, but even people from the marketing department in DDN are pretty technical because you guys have, you do have a really rich heritage in high performance computing. So what are the similarities in terms of how you leverage that high performance computing in this space in this media space and what are the differences? Yeah, you know it's fantastic, right? If you look at our heritage in HPC and HPC for so long was way out there by comparison in terms of performance and quality required as you mentioned moments ago, right? But now the two things are really just coming together, right? When you actually look at Livermore Labs as which customer of ours, right? And the data that they're doing and the processing that they're doing you can literally just go over to Technicolor and see almost exactly the same thing going on, right? If you didn't know what business they were in the amount of data flowing in and out and the requirements are very similar. Yeah, so you've done some other interesting things in HPC, some interesting buffering and some of that stuff's not cheap, you know? Are you able to bring that down? We were talking about the economics. Are you able to keep pace with things like the cameras and so forth? Is the storage industry in general and DDN specifically able to keep pace with those shrinking economics? Definitely so. And it really looks at the total cost of ownership, right? So yes, will the system be expensive out the door? It will be. When you look at the ease of implementation, right? The ease of scale up and scale out. The ease of maintenance. It really comes down to the end of the day to be a much better solution for them. It's about having 24 people trying to run a storage system versus one guy trying to run a storage system and being able to store the same amount of stuff. And a major theme here is workflow. You're hearing a lot about the productivity of whether it's editing, post-production, and so forth, and you're mentioning a solution. What are you seeing in terms of the productivity impacts that customers are demanding and how are you responding? Yeah, well, as I mentioned before, that non-linear editing situation is interesting at the moment with the changes that are happening with Apple and the things that Adobe was talking about. We were talking earlier about with Adobe. And so what we're finding is that people are looking to experiment, right? They're trying to think about where they're going to be 10 years from now in that and what kind of suite they're going to be running, what's going to be their workhorse. And so it's a really interesting point in which to have a workflow that allows for all of those to work together. So how about cloud? I mean, it's in your title. What is cloud to DDN? It's a lot of things to a lot of people. Give us your angle on cloud. Yeah, so Jean-Luc yesterday spoke at the Cloud Computing Conference here, which is a sub-session of NAB. And it really is about collaboration for creative people. It's a follow the sun process, right? I can literally get people in LA starting a process. It's going to go over and worked on in India. It might get worked on in London. It gets worked on in New York and it's done by the time they get ready in the next morning in LA. So it's really about collaboration and follow the sun. Has it been, what's the time to market advantages? And obviously it's pretty obvious when you can follow the sun, but what are you guys seeing from the customer base? Where are they in the adoption cycle of this follow the sun strategy? I'd say it's still in its infancy. I really think, you know, there are a lot of kind of clunky things in terms of how they're transferring those files and moving them around the world. And fortunately at this point I'm making it relatively difficult. But I think in the very, very near future that's going to be much more of a reality. Literally saving something in New York, opening it in India the next day. So what do you think of the Intel studio experience? It's fantastic. This is beautiful, isn't it? This is really wonderful. And you know, I'd like to say thank you to Intel. We're also taking part in their storage zone QR scavenger hunt. And we've had a lot of people come by the booth because of that. They're looking for storage solutions. So thank you to Intel for that. Yeah, you guys have been a long time partner of Intel. I mean, have enabled your business. I mean, talk about that a little bit. Yeah, so Intel has been a fantastic partner of ours. Several years ago we made the switch over to put all of our technology on Intel. And that was a big decision. It was a huge decision. And it's worked out really in our favor. So there's no doubt. Thank you for being here too, but it's also worked out very much in our favor. Okay, all right. Well, Dedean, thanks for coming on. Appreciate you saying hi to John Luke for us. I certainly will. Michael, pleasure. Thanks for coming on theCUBE. Okay, appreciate it. Dave, what do you think about the storage wars now inside here, inside any of you? Well, we're seeing, like you said, a lot of disruption. It's a very storage intensive business. Everybody's going after big data. Everybody has a little different perspective. I think Dedean actually has one of the most interesting perspectives. It's not big data washing. A lot of times we say big data washing. I think organizations that are coming at it with really an understanding of what's required and really with a clean slate approach are going to do better. And I think the advantage that you have, coming at it from the HPC industry is those two worlds are coming together. And so I think that is really what is going to disrupt the whole storage business within the media and entertainment space.


was a bright spot for the Yankees once again tonight. Two more hits for Stanton in what has been a terrific postseason so far including another homerun tonight his fourth of this series. Let's hear from Stanton. What's the feeling in the clubhouse right now after that loss knowing that you're just a game a loss away from elimination. Yeah I mean we know we know what we need to do it's gonna be a tough battle but you know we know there's no other option so what we we know what's in front of us and that's all you can do I mean this what's happened this happened and and we know we need to get some wins for sure I mean we it's when now we're gonna go home so it's okay to have that mentality backs against the wall but you guys show up you got to you know we got to play out take a next one from Dave Lennon. Joe and Carlo things kind of changed so fast for you guys you had been hitting so well you had been kind of on a very on a very good role and then you just kind of ran into it a little bit the past couple nights just a situation of the rays just throwing some really good pitching at you guys or what's kind of been the past couple nights? Yeah I mean they've thrown some good pitching at us but at the same time you know we're great hitters so yeah I mean it's been we haven't put up like we should the last few games but it's okay we know we need to do I mean what's in front of us is in front of us. Thank you. Next one to Bruce Beck. Joe Carlo do you feel that the baseball looks like a beach ball right now because you're in some kind of groove? I feel good. Hitting is difficult either way you look at it but yeah just just really sticking to good plans I'd say over over anything. What kind of confidence do you have when you step to the plane right now? Stick to my plan I'll be fine you know if you fall into his bad things happen. Next one to Brendan Cuddy. Joe Carlo how do you think this team responds to a backs against the wall situation? We we gotta come out we gotta come out swinging we got to come out exactly like that like our backs are against the wall and you know it's now or never so let's go.


Welcome to Entrepreneur India. We have Shreen Mansanga, founder of Needledust with us. Hello, welcome Shreen. Nice to meet you. Thank you so much. It's so nice to be here with you guys. Shreen, so please share with me your journey. How did you start Needledust? So Needledust started about five and a half years ago and it was purely by accident. I finished my journalism school and I came back to be a journalist, an investigative journalist. And I met my husband back then and we decided to get married and I took some time off. And I had a very clear picture, like any other girl how you want to look like as a bride. I put my focus with my shoes and I was actually just pleased. So and I thought like, you know, I had like a certain design in mind, which is still on my shelf. It's called Forever Yours, the gold congruent design. And I had it in my mind and I thought it would be so easy for me to find it, you know. Like if it's come to mind, my head of course it's out there. But when I started looking for it like in just bed and find the one I was looking for. So I said, okay fine, I'll get it made, no big deal, they'll make it for me, right. So when I sort of like, I started like going around with my sketch saying this is what I want. They said, I'm going to put so much effort in one, you know. So at that point, I started looking for like an artisan who would make me the shoe. Anyways, you know, after like certain like two and four, I found like artisans, you know. So I got my embroidery done, like, you know, I purchased materials and this and that and put my shoe together. Remember when my friends and family came from, you know, all over the world and other parts of India for my wedding. And everyone loved it. And I just an idea came to me. I said, hey listen, I, there is, it took me such a long time to find the perfect pair. And there might, there might be girls out there like me that would want to wear jitties but it's something different, something extraordinary, something beautiful. That's one available in the market. And I want to do this. I want to explore this and that's how needle art started. That's one different chain but like how did the business start? So I don't come from a design school. Yeah. I come from journalism school and obviously starting was quite a learning experience. Every day, like it's, I learn every day on my job, you know. Back then it was just more about finding karigas, you know, like finding artisans. I had few designs in mind. I wanted to pop pretty exciting jitties but I had no idea where it was going to go. I remember starting with just like a hundred shoes, you know. For like footwear, that's just six designs and it's six sizes and five sizes back then. It's not like a huge quantity. And I thought fine, you know, I'll start this. I'll take the plan and if it doesn't happen, I'll give it to my friends and family, you know, at the most. And I manufactured hundred shoes and I remember we sold them through an online portal which is not needle art. There was another online portal called Exclus3.n. And I sent it to them and I said could you just share it on, you know, like put it on your e-commerce website. And they put it on the e-commerce website and I had no idea what to do. What it was going to be on the seventh day of us going live on their website. I get an email from them saying congratulations, we're sold out. When can we get the next stock? And there was no stock because I didn't even think I'll sell these. And that's how it started, honestly. So your business was earlier, always online? Yeah, so I started with an online portal because I didn't know if I could invest. I didn't have the guts to invest in a brick and mortar store like in a retail space, physical space or even an online store which was only needle dust. So I didn't have the guts to do it and I didn't know whether it was worth the investment. Because obviously when you start something new, you know, it's a brand new idea. At that point, nobody was doing jitties, designer jitties, nobody was putting that sort of effort in jitties as a footwear which is a dying handicraft. So for me to like pack that investment was a huge, you know, sort of like a, it was confusion, you know. But when we went along with another e-commerce website that sort of gave me an assurity that, yeah, there are people out there who love it. I started getting a lot of personal messages. After that I got, I picked up a really tiny little store in Sharper Jart and it's the same store I have today but it's just become four times its space. And that's why I opened my first needle dust store which is a really tiny one in Sharper Jart and that's where I started from. And then we went on to open our exclusive online store needledust.com where now we shipped to over 200 countries. So a lot of success that you received was also because of social media. Yeah. The way needle dust Instagram pages for example. Yeah. So what do you think the role of social media was in your business journey? The new age media is massive for startups, massive for people with newer ideas and I tell people this all the time today when we have discussions with real friends, family. I always give a lot of credit of my work or the success of needle dust for social media. I think because of social media and now needle dust ships to 200 countries the fact that people know more about my product from sitting in Italy, Switzerland, Zimbabwe, you know, China all sorts of places in the world like things that I never imagined needle dust could reach. I mean sometimes you get orders online and I'm like wow and from local names not NRIs from local names and I'm like that's incredible. So the jitties reaching places where we never imagined it to reach and that is all thanks to social media. You have no designing background stream so what were the difficulties that you faced? I had never like, I had never sketched, you know. So like for me to translate my idea to the cardigans. So I had to learn, you know, I sort of had to learn basically sketching I had to basically learn how to put colours together. I didn't know, I didn't know words like kha kha or dark and who's. These are things I learnt from the cardigans and what's really amazing I feel like through my processes the guys who actually do the work, the artisans are your biggest teachers and then nobody can teach you what they do. Even today sometimes they come up with some beautiful ideas and they're like mammy's ko aise karein, I'm like yeah show it to me and then it becomes beautiful. Your jitties are very popular among Bollywood stars. So who was your first Bollywood client and how did the client grow so fast? The first client, celebrity client was Sonim Kapoor and it started from there, you know, we went to Ria Kapoor and Ria Kapoor put me on her team and we sent them like the first product and we just launched Needle Dust back then. I remember they Instagrammed us, you know, Ria and Sonim and that was amazing. So it started from there and now it's just very natural. We just, we concentrate on doing beautiful designs and we never thought like we were a part of, we did the wedding Padmavad, student of the year post-launch and then we did Priyanka Chopra's wedding where we launched the Abhujani Salip Khosna line. We did Deepika Padukone and the recent wedding where we did Mehendi gifting for them. Sophie Turner and Daniel Jonas ended up wearing our jitties. So it was really amazing. So you guys never have a creative block? We do, of course we do, all the time. Like we have a creative block all the time because sometimes what you have in your head doesn't necessarily translate onto like physical shoes, you know. And then you're trying so hard to crack it, you're trying so hard and this doesn't happen. And then what we do when a creative block happens is just stop it. I'm like let's stop it, let's take a break for two days and then come back with fresh ideas. And yeah, that's what happens. And anything else that you would like to share about your journey? Neela's store like design happened to me absolutely pure accidental and by chance. And I just want to say if you have an idea, act upon it. No idea is too big or too small to give up or not to sort of take on. Okay, so this is Shreena Maan Sangha with us. Nice talking to you Shreena. Thank you Daira. It was so lovely speaking to you. Thank you so much. I've had a great day. Hi guys. I'd like to wish all the ladies out there watching me and entrepreneur India a very happy women's day. I hope 2020 brings in a lot of newer cooler ideas, a lot of new women entrepreneurs and a lot more success stories this year.


Hey guys welcome to SSUnitech, Sushil this side and this is continuation of Power BI tutorial. So today we are going to discuss about the ribbon chart in the Power BI. So go to on the Power BI desktop and we will see in the practical about the ribbon chart. So this is our project by which currently we are working on. So here inside the visualizations you can find out the ribbon chart. So click on that and let me try to make it bigger like that. So here in the properties you can see the access lesion value tooltips. So in the access I want to see the country names. So let me drag and drop the country name in the access. After that in the values I want to see the subtotal. So here you can see the subtotal. So let me drag and drop right here. So here this is very similar like column chart. So we don't see any difference right now because inside the values we have selected only one column. Now let me try to use the tax amount as well. So we can drag and drop in the values. Now here you can see the difference. So in the column chart we will not see with variation like that. Here we are having like that. So this is the ribbon the first one that is United States. Here in the top side you can see the column and in between two countries we are this highlighter that is not present in the column chart. So this is the difference about the column chart and ribbon chart. And here you can see the lesion and tooltips you can use according to your requirement. So thank you so much for watching this video. If you like this video please subscribe our channel to get many more videos and if you have any question then you can drop your question on the comment box. I will try to response on your questions. Thank you so much. See you in the next video.


Welcome back to my YouTube channel. This is Daniel Russell here for today's video. I want to talk once more about the topic of subtitles and stuff you can do by adding foreign language subtitles to your video content up on YouTube. Now, why is this something you might want to do? So when we're talking about subtitles or captions in the world of YouTube, there are really two different types of subtitles. The first type of subtitle is something called an auto-generated subtitle. So if you upload a video to YouTube like the way I'm going to be uploading this video to YouTube, after a certain period, sometimes it takes as long as 24 hours, sometimes it takes a few hours, you're going to see if you click into subtitles, auto-generated subtitles. Now what those are is basically YouTube has at its disposal a lot of computing power. And when it's bringing your video content into its processing workflow, it's also running it through a speech to text recognition algorithm that's going to take the words you're saying and automatically add text, and then all it has to do is automatically adding subtitles. Now I've done a few videos recently on how to do this using Cade and Live. In other words, if you actually want to do this at the pre-upload to YouTube stage at the pre-distribution stage, you can also use a open source, completely free speech to text editor to automatically decipher what you're saying and automatically add subtitles. So the truth is, you don't even need YouTube in order to avail of this pretty cool technology. Even if you're uploading your videos to Vimeo, your corporate website, or just some other website, you're able to today avail of this powerful tech for free, which is pretty cool. Now the auto-translated subtitles are basically as follows. When you have one manual or human-generated subtitle on YouTube, you're going to get the option to automatically translate from that starting subtitle file to different languages. Now remember I said there's two types of subtitles on YouTube, one being auto-generated subtitles and the second being subtitles that are human created. Now you can create your own subtitles on YouTube in one of a few ways. You can go through a video you've uploaded. You can add your subtitles or you can upload a finished subtitle file in the SRT format, which contains the subtitles you want to appear on your videos alongside the timestamps and YouTube will parse that and know when to display the subtitles. So if you've gone to that stage of adding a manual or human-generated subtitles, you're able to add automatic captions in order to allow people who speak a different language to be able to understand your content. Now it's true that there is such thing as auto-translate on YouTube. That's a future that's currently being rolled out by YouTube on mobile devices. Auto-translate allows users to take an original subtitle file and run that through an automatic translation algorithm and they'll have that caption display in their local language. Now the difference between the two is actually pretty minimal. If you're using the auto-translate capability on YouTube as a creator, my belief or impression because the translation process happens pretty much instantly is that you're actually using the same translation engine as YouTube uses to auto-translate subtitles. The idea behind auto-translating subtitles is that you can do a very good first pass and if you know the language you're able to go into it in more detail. Now another advantage of adding translated subtitles is a lot of your YouTube viewers may not realize that auto-translate is even a feature on YouTube because it's as I said currently only being rolled out on mobile. They may never have gone into subtitle options and seen there was an option that they could automatically translate your subtitles into their native language. So creating a few different subtitle files in different languages is kind of a way of making it easy for your viewers being like hey there are subtitles in Arabic, there are subtitles in Russian. Now one final question before I show how to do it because it's really pretty quick is do viewers know that these were machine generated machine translated subtitles? The answer is no. Once you create a subtitle file in Arabic or Russian or Dutch even if you are using the auto-translate functionality to create it, it's going to look to your viewer as if you actually know those languages or hired someone who knows those languages and actually created human source subtitles. So just be aware that I wouldn't say it's misleading but it has the potential to be a little bit misleading if a native speaker expects native quality subtitles and they get machine translated subtitles which are always going to have a bit of errors but the idea from YouTube's perspective is if we start from one machine generated subtitle the auto-translated subtitles will be good enough and that's why in my opinion this is just speculation YouTube will not allow you to create machine translated subtitles if you don't have one human source subtitle. In other words you can't combine auto-generated subtitles and auto-translated subtitles to get to a foreign language. Now I know that was a lot of information that is a little bit confusing but that is basically how subtitles and translations currently work on YouTube. Now to take this from theory to practicality this is the last video I uploaded to this YouTube channel of me eating hummus and drinking beer and finding lots of good coffee in the old city of Jerusalem. Now I did add subtitles to this video in places where it was kind of hard to hear me so it's not a complete subtitle but I can it's actually most of my commentary has been subtitled. So you can see when I click into subtitles on the left of my video editor I have video subtitles and I have my subtitles in English. Now I can edit these subtitles or download the subtitle file or do just about anything with them. Now if I want to roll out a translated version of the subtitle I simply need to click on add language. Now of course if you're going to be doing this it kind of makes sense to think well what languages might be helpful for attracting more people to this content. I'm going to go for Hebrew and Arabic because it's a video about me checking out places in the old city of Jerusalem and the two major languages that's going to be relevant for are Hebrew and Arabic. Now once you get to the stage of being adding auto translated subtitles you can also add video descriptions in those foreign languages and that's there is no functionality I think for automatically doing that but let's start the process anyway let's start let us start with Arabic so I'm going to click on add language I'm going to click on Arabic and now we have Arabic is appeared here and we have two things where we can click on add one of them is title and description and one of them is subtitles so in title and description as you can see that there's a human source translation and this is where you're supposed to add your translated version now it's not actually mandatory to do this in other words you can actually just add foreign captions and not add an accompanying title and description in that language but if you're if you're adding foreign subtitles to try increase the amount of people viewing your content it would make a lot of sense to also roll out translated titles and descriptions let's just do subtitles for this one and I'm going to click on add in subtitles and now you can see there is a button called auto translate and if you hover on the about text it says translated subtitles from the original video language either auto generated or manually added to the chosen language now I generally don't get that option when I have no manually generated subtitles so it the text sounds as if you can use both but in my experience so far it hasn't appeared in videos unless I actually add a human subtitle so I'm going to click on auto translate here and now you can see at all the different times timestamps in literally one second it's automatically translated my captions into Arabic and I can just click on publish and that's going to add these captions so you can see it's a super quick process literally takes two seconds I'm going to do the same for Hebrew ad language uh Hebrew now the good the cool thing about this workflow of course is that if you do um if you do speak that language you're translating into you can do auto translate just to like get a quick start on the subtitles um and then you can just like fix them manually here by like clearing up typos or etc etc but it'll still be a lot quicker than doing those subtitles from scratch I'm going to click on publish as well for Hebrew subtitles for this video I'm going to give it a second to do its thing and now if I click into this video on YouTube I'm going to just pause the video for a second and you can see if we click on our cogwheel we click into subtitles we have Arabic and Hebrew as well as English um availability of subtitles so that's how it can be done it only takes a few seconds if you do want to go to more effort of really really improving those translated subtitles as I said you can fix the auto translated subs you can add your own title and description and this would be a great way to open up the possibility of people in different countries who speak different languages being able to watch understand and engage with your video content on YouTube hope this video was useful if you do want to get more video content from me do please feel free to subscribe to this YouTube channel


My name is Ingrid Steak. I come from Oslo in Norway. I'm here to do research as part of my PhD. It's been a very great year. I've learned so much both about research, just work in a research environment like you have here where you collaborate with the engineers and the statisticians and the attendings and the other fellows and the research assistants. We don't have an environment like that in Norway. I've been working in the lab on different ankle projects. There's been so many memorable moments here. Going skiing with Dr. Clanton early in the morning, hiking, bicycling. Travel to Moab, that was beautiful. It's so different from Norway. Also, we have been a very nice group of people getting known to each other. Thank Dr. Clanton for welcoming me. I think the whole year has just been fantastic.


Hey everybody, welcome back to the patch and switch consultancy and user guide to digital Microsoft Ignite today's topic digital breakout sessions Okay, so we already talked about registration. So that's important to get the full experience But picture this as basically after the keynote segments take place the digital breakout experience. You'll have six by six or six by eight or six You have six simultaneous breakouts, let's just say let's keep it easy and then okay last approximately half an hour There is live Q&A Moderate Q&A with the individuals that are going on and you're watching on the screen But then there is a brief pause of 15 minutes or so for an interstitial that can take place and then the next breakout starts and then another interstitial and then the next set of breakout starts and then another Interstitial and then the next set of breakout starts I've I've lost track of how many breakouts we're talking about that's not 48 hours worth But we get what you're saying it's it just repeats. Okay, so that's important to note But either way six simultaneous Tethical sessions you can go watch and interact with is taking place in the digital experience of a digital breakout But they are repeated as a block throughout three different time zones So if you miss your particular one you wanted to go off and see you could always catch it again in another time zone If you wanted to stay up late or if you wanted to go wake up early You'll be able to still have access to them and one of the cool things about that is you do get that Q&A Experience there where you can actually ask the the experts behind The session your questions. So that's that's that's one of the great things But in order to do this you must be registered So head on over to my ignite up Microsoft comm get registered and of course if you want more of the end user Guide to the digital Microsoft ignite experience head on over to the blog post below because we've got more videos to come


and welcome everybody to another OpenShift Commons briefing. Today we're going to be talking with Audrey Resnick who is a data scientist here at Red Hat and we're going to talk about managed services and data science and her topic today is what's the deal with managed services and model delivery. So it's going to be a bit of a technical overview. If you have questions ask them in the chat and we'll relay them to the speaker at the end of this session. So Audrey take it away introduce yourself and let's hear what you have to say today. All right well good day folks. My name is Audrey and it's really a pleasure to be able to speak to you today about managed services and model delivery. This is going to be a gentle introduction on to what managed services are and how they can be used to gracefully deploy your model into a hybrid cloud. So the items that I'm going to talk about today are exactly what are managed services, who cares about them, where do I find managed services and what do managed services have to do with model delivery specifically. So we'll kind of go through a use case and then the question that is really important are these managed services easy to use because whenever somebody tells me about something new that's usually the first thing I ask is well it sounds good is it easy to use. If we take a look at what generic IT managed services are that will kind of set us up for the discussion just to give your brain a chance to flow and get into the idea of services. Managed services in generic IT are the practice of outsourcing the responsibility for maintaining and anticipating a need for in a range of processes and functions and that's all in order to improve IT operations and really to cut expenses. So we're going to look at everyday examples of managed services that you would find in a normal IT organization and this will give us a really good baseline in terms of not only what these services are but we'll get you thinking about what managed services could be available for data science. So the first thing that we have and I think everybody's familiar with is help desk then we go on to looking at equipment installation and along with that hardware maintenance also with equipment installation it's also moving those moving services that could also be placed into that. Firewall and security we need to be able to keep our organizations safe and secure we don't want people breaking in. We do that part of the ways by keeping up to date on antivirus patches various updates systems monitoring is a big part of the services because we want to see how our systems are performing are they are they performing well enough with the amount of users that I have or that we have if we add more users are those the systems being overwhelmed and speaking of being overwhelmed what about disaster recovery what happens if our main facility or our shop is wiped out do we actually have the capability to bring those services for our customers back online elsewhere and what about managed backups I mean that's the part how we get to disaster recovery is we should be having backups of our vital information that's just a small amount of managed services and there are so many many many more and today in data sciences we have the complexity of adding cloud as part of the platform that we work on and there are a number of services that come with that therefore the the security the data repos the servers the communications sharing services everything that we have is just a little bit more complex so kind of understanding now what generic IT managed services are let's look at what kind of what I call managed services for data science or the services that would make sense in data science would be and how would these services help us deploy an AI ML application into our model into production whatever managed services we create we have to allow the data scientists to focus on building their models while building their solutions data scientists being one of them I want to experiment really with the latest bells and whistles I don't want to deal with upgrades I don't want to deal with supported versions I don't want to have anything to do with compatibility issues I just want to focus on my solution however this does not mean that a data scientist is able to walk right up to IT DevOps hand them their laptop where they've been creating an AI ML model in isolation and say okay I'm good I need this model to go into production tomorrow I shouldn't do that I seen people do that we need that collaboration with IT DevOps and we need these services and methods that data scientists can use to work with DevOps to put their models easily into production and monitor performance so in data science there are a few steps that data scientists are interested in when thinking about creating an AI ML model to solve a particular problem and I feel that these steps would make good managed services so that's what we're going to go through and look at this all starts with data acquisition so we're looking at extracting and transforming the data we can integrate streaming data from OpenShift to Apache streams for Kafka are reaching out across the hybrid cloud to pull in data for analysis from multiple platforms in data services and we can use services open source services such as Starburst Galaxy to help us curate our data just one example the next thing you want to do is to be able to run experiments and create the models we can provide a notebook environment for model experiments and for the customers that would like to access the curated data science packages we have Anaconda commercial edition integrated for those that are looking to take advantage of things like auto ML and then make use of things like IBM Watson studio once you've coded these experiments and you've determined that they will fit the model that you have and it looks like your model is good and primed you want to be able to access any hardware accelerators to speed up the time to value we partnered with Nvidia to provide a GPU capability and what we also then want to do is go ahead and then deploy these models as services once you have your models developed you can use our source to image templates that we have or or use OpenShift Pipelines to deploy and do endpoint for testing you can also use seldom deploy for model serving then we want to look at monitoring the models and tracking performance we can continue to use things like seldom deploy or Watson machine learning and Watson open scale for any of the model monitoring and performance tracking to know when you need to kind of retrain your model and redeploy it and when you look at this overall picture that I've set up here or this path keep in mind that for any IT ops that's looking at this or DevOps this flexibility can really be a nightmare because they want a reliable stable reproducible environment for their customers which we hope that we can provide her so now that we've defined these this set of managed services let's look at who beside data scientists would care about these services it's not only the data scientists but also the data engineers and IT ops that care about these services and alongside with these managed services there are other things that kind of really fall into place nicely you want to have an AI ML model operational life cycle and that's kind of what I sort of outlined in the previous slide the data scientists sort of want an environment services in which they can not only do development work using kind of the latest bells and whistles that are open source which is awesome but also an environment that they can deploy their apps into production and this environment should contain that exploration of data and the monitoring of deployed models and applications the other thing that you want to care about is that production ready platform and this platform has to be something that IT ops feels really good about because the managed services as I mentioned can be a nightmare for IT ops is they want something that's reliable that's stable and reproducible for their for their customers the other item which kind of falls into place and is is really important to think about is the flexibility to use any open source services that are that look interesting to you when you're actually going ahead and creating a solution so being open source just to remind for it means that it's essentially free to use and that usually with an open source service or item there's a large network of users and developers who contribute towards updates new features offering support for new users and lastly the ability to kind of deploy and portability to move from the platform that you initially developed on the ability to deploy and move your application allows you not to be tied to a particular vendor and I personally personally feel that to be very innovative these days you need to be able to try a wide variety of technologies and services and that means trying out a large number of vendors so that you can create the best product that you can for your customers now with all of these things said there actually is a middle ground where we can make everybody happy at least I feel that there is so let's see if we can create kind of a data science managed services platform that satisfies this middle middle ground all these items that we've talked about so we're going to start with infrastructure the hybrid cloud platform and we'll go for a hybrid cloud so that we have things on-prem inside our own network maybe we'll use something as Amazon web services as our public cloud portion should offer very a very consistent experience across on on premises in the public cloud as well as to the edge locations and all of that has to be efficiently managed by IT operations we need to look at compute acceleration this hybrid cloud platform that we have should have integrations with hardware accelerators such as GPUs to help speed up any of our machine learning model development and inferencing tasks this brings us to self-managed services we could have all these services but what would be really fantastic is to have all of these supported kind of on a self-service hybrid multi-cloud platform and that's the platform that would really go ahead and empower anybody such as a data scientist or data engineer or software developer to be agile and collaborative through the whole process and that's without depending too much on IT operations for individual tasks we don't want to fill out many tickets to say I need access to this or I need this type of service you should be able to go in and self-manage that pick and choose what you need in order to get your job done so here's kind of the conceptual architecture for this AI ML model services so we'll go into kind of a typical project life cycle so we have data engineers that are working on gathering and preparing the data to make sure it's ready for the data scientists to develop their machine learning or AI models and a managed managed service that we could possibly choose to use is a starburst and starburst is a fully managed service you can access your data using Trino there's a premier SQL engine there's fax access that you have access and that flexible flexibility to manage your your data the next thing that we have is the business of developing a machine learning model an example of a managed service here would be something that you could use like jupiter hub that allows you to create jupiter notebooks for experimentation now I say jupiter hub and jupiter notebooks for experimentation because you don't want to end up deploying a jupiter notebook into production please don't do that I've seen people that have tried to do that it's not a good idea however for experimentation when you're first getting started to take a look at what your data looks like in terms of how it pertains to the algorithm that you're developing and kind of looking at how your algorithm can kind of solve the problem that you've been tasked with jupiter notebook something like that is fine and within this area when we're developing the model we need to also be able to determine or add any packages or libraries that we're working so for pandas we may need to use numpy we may or for python we may be able to use pandas or numpy we might be able to choose something else such as tensorflow if we're working on some sort of problem but we want the data scientists at the end of the day to really be able to experiment with those packages so again whether it's tensorflow, pytorch, scikit-learn, any others the whole idea is to have these these tools or these services available so that data scientists can do the experimentation next we have to actually go look and deploy models in an application so again this is kind of part of the model life cycle we want to go ahead and get our model and be able to deploy it and start some inferencing making predictions kind of based on that data and see if what the problem that you're trying to solve is is going to be solved by by what you're experimenting on right now so there are manage self-managed services such as selton deploy which help us build pipeline sorry and actually go ahead and deploy our model the work does not stop there when the model is deployed I know some people that say okay I'm done you have to continuously monitor and manage any of your AIML models that you create in production make sure that they're making the right predictions make sure that there's drift not happening and you're not going to be doing that by staring at a monitor and looking at your model performance through some simple little script that you you've written you want to have some side of services that will give you alerts tell you when the model is drifting so that you can continuously again go ahead and monitor and manage your your model and production to make sure that they're making those right predictions and of course when you do find something that is drifting or something that is not quite right you need to have that ability to retrain those models as as needed so keeping that in mind that that's kind of our ideal sort of managed services and kind of the platform that we go along with it let's actually take a normal or I should say an actual machine learning use case that one of my own colleagues is working on and see if this kind of data managed services and model delivery platform that we've kind of come up with would actually work for that so there is a project being undertaken by one of my colleagues Guillaume Montier for Metro London that has to do with license plate detection so that all has to do with looking at the cars grabbing the license plate and being able to monitor traffic movement car registration and any sorts of licensing fees again through the license plate detection that machine learning model has to have the ability to detect the license plate on a vehicle if the vehicle is angled the license plate needs to be righted and the characters gathered through some ML algorithms that have been developed data can then be stored or read or analyzed through Kafka in this instance I just for the folks that don't know Kafka is an open source software that basically will provide a framework so that you can store read and analyze any of your streaming data so for instance here if we're looking at some of that streaming data and we found a license plate for somebody where something was notably important about that car we could throw an amber alert finally we have to actually go ahead and store that data whether we use an object warehouse or we go back to a vehicle registration database storing that data then gives us that ability to do analysis further I mean can we look at that data and do some analysis on traffic movement congestion parking etc so now that we've kind of gone over this example let's take a real managed services platform and as a data scientist build out this AIML detection service for license plate detection the architecture that we put together for managed services this is my confession time is are the services that a data scientist could use actually exist as the Red Hat OpenShift Data Sciences platform and I'm going to use this platform which we call Red Hat OpenShift Data Science to show you how you can use these managed services that we have that we've actually discussed also discussed discussed to deploy an ML model so this all kind of comes together when a user first starts using the Red Hat OpenShift managed services platform by having everything in one central location or shared UI so that the user can discover and access a variety of open source solution each managed service whether it's Red Hat or a partner service component basically we'll go ahead and integrate along with a series of quick starts and tutorials so that way users can not only work with their self-managed services they can also self-teach or understand things better about that managed services so that they can get started working with any of the components or services and once users have enabled components so in this example in the far screen capture in the back you see that I've enabled Jupyter Hub so that's a component that's going to be available for my use again along with all the quick starts and tutorials that will always continue to be available for for people to look at but let's specifically go back to this Jupyter Hub managed service and launch it and what I'm going to do is I'm not going to walk through a real demo because we know how those happen sometimes but it'll be kind of a canned slide demo so we kind of clicking through things on slides and seeing how this all comes together so again we're assuming that our data set for the licenses has already been curated therefore we begin by using Jupyter Hub so that we can experiment with the data and just a note here just because we use a Jupyter Hub managed service at this point in time it doesn't mean that we can't integrate with any other services that is that are out there or go back we certainly can we have the freedom to do that and you have the freedom and the ability to manage and use as many services as you like realistically when you're developing something there may be other parts of the system that you haven't thought about and these managed services can fill in gaps that you may have not built out in your workflow so we go ahead and we launch Jupyter Hub and we're going to go ahead and create a Jupyter Hub notebook image which means that we're going to be packaging up a Jupyter Hub notebook into a container image that you can deploy to OpenShift and you're going to be able to customize a number of things here you're going to be able to customize the notebook image type you know you're working with a problem that requires you to use PyTorch or does the problem that you're working on require you to use TensorFlow do you just want to use a standard data science image just to do some exploration in this case we're going to go ahead and actually let me go here let me just back up a bit and go back to a container just in case folks don't know what a container is a container you can think of it as a single entity or unit that combines your entire runtime environment which would include your application any of the dependencies libraries any of the Python libraries that you may be using other binaries and any of the configuration files needed to run your application it's all bundled into one package and by containerizing that application platform as dependencies your differences in your operating system distributions and your underlying infrastructure are abstracted away and that's really good because that means that it's something very portable now that you could use on prem that you could probably use in the public cloud say I don't know like AWS so again containerization just plead provides that clean separation of concerns so that developers can focus on their application logic and dependencies and then of course the IT teams can focus on the deployment and management of that container without bothering about the application details such as a specific software version or configurations to an app in this case I'm looking again at these standard notebook images that I discussed and here I'm going to pick a base image that would contain the majority of the packages and libraries that would be needed for the license plate detection so I'm choosing a TensorFlow notebook image we can specify a deployment or container size that we feel that we would need for our machine learning model we're going to choose a large container size with limits of 14 cpu and 60 gigabytes for memory requests you have the ability to add one or more gpus based on the type of data analysis that you're doing and of course in the ml code that you're working on in this rendition we won't use gpus but remember we can always go back and recreate our notebook image with different options if which you saw and then users also have the ability to add environment variables that they would need on your project so this is an example of adding an aws s3 access key id environment variables to access an s3 bucket so access your data in aws and we're going to then once we finish adding in the secret access key environment variable and its value we click the start button to spawn our new geopoder notebook image and that can take a bit to spin up in that time being to see what is happening you can always click on the event log to get a better idea of what parts of your image are being rolled out and where you are in the image build process so now you're in your jupiter lab environment and as you can see it's a web-based environment but everything that you do here is in fact happening on the red hat open shift data science cluster that's sitting on aws this means without having to install and maintain anything on your computer and without disposing a lot of local resources like cpu and ram you can go ahead and still conduct your data science work in this stably managed environment so let's go ahead and populate jupiter lab right now with our current license plate get repo so what we'll go ahead to do is go up into the main menu we'll choose get and we'll choose clone a repository and then we'll enter the name of the repository and press the clone button to clone the license plate workshop repository note you could be asked for your get credentials so you'd enter your credentials and then again press okay to continue and what you'll then see is that actual license plate workshop repo files appear under the the name pane in the left hand side of the actual window we can then go ahead and open up any of the the notebooks and or we could be creating notebooks in this case i'm showing just an example of a notebook that we use to recognize and extract the license plate numbers from car pictures and we installed some libraries a little earlier on in this jupiter notebook that weren't part of the container image that's also something important to realize is that not every image will be totally perfect for everybody there may be additional items that you can install that's very easy to do we'll go ahead and experiment with our model and at the end make sure that we can detect a license plate number so along the way we'll go ahead and package the model that we end up creating as an api and um earlier on before we got to this point we learned how to kind of create the code that would be able to extract the number from the given license plate but of course you can't use a notebook like this in a production environment i do know people that have tried to use jupiter notebooks in production it's not a good idea it's not a good idea therefore we're going to package this code as an api that you can directly query from another application and we do this by creating a flask application um few explanations though the the code that we wrote for this particular problem that Guillaume was working on ends up all those jupiter notebooks that you saw previously end up being repackaged as a single python file with um that we call prediction dot pi and basically it's just code that was in all the cells of the notebook and put together within a single file and to use that code as a function that you can call you just add a function call say predict that takes a string as an input which would be the name of a picture it does a recognition and sends back the result and you could open the file directly in jupiter lab to see for yourself um but you would be able to recognize the previous code with the the new function added then what we would go ahead to do is launch our server um so in this case here we're just going ahead and launching it locally and we could go ahead and then test our flask application and see if it's working and it looks like our status return that it was okay so now that the application that we verified that it's working we're ready to package it as a container image and have it run directly an open shift as a service and when you do that you're able to call that service from any other application so we'll go ahead and we'll build that application inside of open shift which means we'll go to our main open shift dedicated platform and within open shift we want to make sure that we have a project namespace a setup for us to work in and I just called it user one project because my brain was dead but we have um that project namespace now set up for us to work in we go ahead and we're going to import our license plate code from the GAT repository to be built and deployed and we're going to select a number of options to create a deployment for this this model most importantly we want to be able to create a route but as a URL through which we'll be able to access our application this automated build process takes a few minutes then open shift we'll go ahead and deploy the application and in this case we ended up with that rote that I was talking about this again will be the url that we'll use to send images to so we're going to go ahead and test we want to actually test again to see that the deployment actually works we have the application listening at the at the route that was created during deployment and we tested by simply clicking on that that route link that we saw previously or copy or pasting that URL into a browser window once we go ahead and do that we want to test our deployed AI ML application we can also test our app status through curl or invoke a web request we definitely want to be able to upload images but as our application is now a REST API endpoint there are multiple ways that we can upload images to it we can also run this app from a jupiter notebook who would have thought in this case we'll go ahead and we'll add an image and I'm just calling it card dot jpeg just a photo of a car with a license plate and I'll also go ahead and add that URL or route that I created an open shift and if I go ahead and run the cell I'll see that the prediction and I got a screen capture the car so that you could see what the license plate number was that the prediction came back with the u69 yde which is actually correct so now that we've done that let's take a look and see if the options that we're talking about in the managed services platform that we originally put together are actually there and they are again this was a conceptual architecture for managed services and model delivery that that we were talking about but again this is actually the architecture for the red hat open shift data science platform and as we discussed earlier we have that typical AI ML model or workload lifecycle from gathering and preparing your data developing your model integrating your models and app development and doing some model management and in the bottom the the gray area that you see is the managed cloud platform that's provided either by red hat open shift dedicated or red hat open shift service on aws initially aws right now is the public cloud for launch of this service I will be looking at azure in the future and we do include the nvidia gpu support and then of course in the red hat managed cloud services we provide our core red hat open shift data science offering so that's going to have jupiter tensor flow pytorch sourced image for publishing and also tie-ins with other optional add-on cloud services things like open shift streams for apache kofka and our open shift api management service for optional launch partners we do include the service such as starburst for data access and prep and of course I mentioned anaconda for package distribution repositories and then we also have software partner offerings like IBM Watson studio and sell and deploy so what did we learn today well I hoped what you learned today that what managed services are and that they are a big deal when it comes to deploying a model because they make the process easier for data scientists to experiment on for the data engineers to curate the data and when the data scientists have built the model dev ops then has the ability to easily deploy monitor the model and as for that fact the data scientists also have the ability to deploy and monitor the model and with that I would like to thank you for your time and take any questions well thank you for this and you you've covered off like many of my favorite subjects one of which is is jupiter hub and and jupiter notebooks and and I love the the the tip about trying not to or to refrain to from using jupiter notebooks in production that might be the thing that I need to be reminded about the most so the the one request we got from the chat was if we could get a hold of your slides to share them with with folks and I let people know that I would make sure I could do that for them but I just wanted to thank you this has really been interested a very interesting approach to it because most of the times if you're a data scientist or someone who dabbles in in research you end up trying to do all of this by yourself or or with minimal IT support so having tried to do that before my previous lifetime and it's it's not easy and it's not fun and at the end of the day when you're working on pipeline delivery you're like I'm a data scientist I just want to work on my freaking code why am I doing this and you don't have to with with managed services for for data science yeah so I think this is like a huge step in the right direction and that you know there's I'm sure there are other managed services too but it's wonderful to see it all working on OpenShift so thank you for for the tour de force today and we'll share this with the folks that are out there in the universe looking to try this out and look forward to having you as new features and functions come available to talk us through those as well so many thanks for your time today thank you for having me on board remember folks questions bring them on in you can always look me up on LinkedIn and get my my contact through there and I'd be happy to you answer questions perfect all right thanks everybody and take care and we'll talk to you all soon