Hey everyone, how's it going? Welcome back to Nintendo Prime. We got four big stories for you today But before we get into it Yeah, we do have a current giveaway going on you get under the description or the pink comment to enter also I do have kind of a secret big giveaway planned for the holiday season I know many of you out there are struggling I just want to note right now that the only requirement that's going to exist to be able to get into our Massive holiday giveaway is gonna be just being subscribed to the channel So yeah, just subscribe and by the way set your notification bell if you feel like it as well because we have a lot of content Coming your way that you're not gonna want to miss that being said Let's get into today's news So our first story I need to give a bit of a warning on this story does contain foul language It is not my words. It's theirs. We do allow obviously some More mature language, I guess we'll say when we're doing our live streams But for videos like this, we'd like to kind of keep it mostly clean But unfortunately, that's not the case with the story I'm not gonna be bleeping anything out because I don't believe in doing that if YouTube wants to ding me and take away My ads it is what it is. What matters is the actual story in full context. So David Jaff has been in the news Because he has been insulting Metroid dreads design specifically the fact that walls and you know Floors and ceilings that you can shoot and then destroy are not marked. They're not no visual Indicator on the actual wall or ceiling or floor that it can be destroyed He thinks this is really really horrible design.  He hasn't done anything in a while in the game industry of note But he is one of the main minds behind twisted metal and God of war So he is an industry veteran and he basically called up mercury steam and Nintendo for being really bad with game design Obviously fans didn't take too kindly to this and explain to him the many ways he was wrong including this That the game tells you to shoot at walls and then presents that exact mechanic to you right away So it's one of those situations where did he just forget what the game told him to do and it does all the time I don't know but Let's just say We have his response and we're gonna give it in full light He ended up posting his overall response by the way He's doubled down and tripled down on his stance this whole time But he had a response to people who disagree with him in particular fans of Metroid And we need to read this to understand More of his mentality so he put this response up at Neo gaff and here's what he said I don't imagine myself to be anything that sounds like a you problem pal and I for one am getting sick and Fucking tired of insecure gamers who act as if they do know as much as working Professionals I've had hate games and failed games But you ain't had any games at least most of you So I'm sorry if it insults your ego, but I know shit You don't just like you know shit. I don't about your line of work. It's not a brag It's just how experience works.   I have a channel about games in game design I'm not looking to do anything other than express my views on my channel now Do my views happen to be based on decades of working in the industry as a designer and as a director slash designer on a few Really big games? Sure, and so I use that knowledge just like I'm sure many of you are using your background as someone who may have played Every Metroid since the NES version we all bring our own history to our ideas Content work and life in general as for the multiple room thing. I accepted. I was wrong This is a point that you know, he's trying to give himself credit for And admitted I was remembering it wrong in my last video I remembered it wrong and instead of the 20 to 30 rooms I recall being open there were only four and I said I was wrong which I was so what I think some of you all are Projecting when you say his ego won't let him lose or change his mind I'm wrong a lot about shit on my channel and when it's pointed out to me I take it back and as needed apologize and in this case I was wrong But I still stand by the other complaints. I think it's bad as I You don't that's great agree to disagree But I got to say I've never encountered such a whiny ass fanbase as I have with Metroid fans and I say this as a Nintendo fan a guy who is Excited to buy Mario Party next week and review Unseen because I love the series and the characters that much a guy who bought Metroid Excited to get into it as much as I was into Link and his adventures Sadly for me.  I'm not even sure how to react other than the same He has a massive Disrespect for gamers and gamers opinions on games I Can only say the following and I can respect his opinion that he thinks the walls and the ceilings and stuff should have visual Indicators on the actual walls and ceilings themselves. I get it It's it is something modern gaming has been doing for a long time and while it's a bit hand-holdy It is what it is. Some people are just going to prefer that method versus Metroids which doesn't necessarily give you that sort of visual indicator We can argue there's other visual indicators, but it is what it is and it's okay that he doesn't like Metroid That's not that's neither here nor there But I will say this David Jaff said something controversial about a game The controversial thing he said was not very well defended because he showed a gameplay clip showing off You know a hidden area that he how the hell is he supposed to know about it and as he was playing in Metroid Dread He was playing it not only poorly which I play games poorly But he wasn't even aiming in the 360 degrees or 180 degrees. Hope you want to look at it Way that you can a metrogen. He wasn't using this laser sight and he was having a hard time shooting enemies fine I get it. It happens, right?  You don't have an excuse for Ultimately to me this just means David Jaff is not good at games that are difficult that don't tell him Exactly what to do 100% of the time, which again is fine But people were obviously gonna be critical because you are a figurehead in the industry Whether you want to be or not, but you keep reminding people repeatedly in your response.  Welcome to the internet David Jaff What do you think happens when you know a controversial opinion that goes against the grain? you're going to get the ire of a lot of gamers and That sucks and sometimes a lot of us gamers will say things that we probably shouldn't but You're outright dismissing the very people that play the game bottom line is you cannot like that game design Doesn't make it bad game design Okay, that's not how game design works. It is a trial and error type of design. That's intentional And taught to you early in the game and clearly millions of people don't have a problem with it Now it's fine that you do and you might feel that your problem with it is bad design Other people are gonna feel in their opinion your problem with it is that you want your hand to help These are both valid opinions you being a veteran game designer Does not make your opinion more valid than the people actually play in the game See this is a fundamental problem with his entire argument He thinks he's up here and you're down here. Oh your opinion is just as valid, but mine's defendable He's not really he's saying your opinion just as valid as well saying your opinion is not as valid as is Here's the bottom line games are made for gamers This is why a gamer's opinion on game design can matter just as much as the people who actually design the games Because who are the people that have to deal with the result of those designs the gamers So gamers opinions on a game design a element of game design in a metro a game is as valid as yours Because they're the ones that have to experience and play it you didn't enjoy your experience with this design choice And I'm just talking about this one game. I'm not mentioning that it's been an every metroid You don't agree with this design choice Other people think it's the right move and it makes the game more difficult and adds it air of challenge Challenge that they enjoy and you don't and that's okay. Why can't we just leave it at that? Why do we have to go ahead and try to make? Gamers sound like their opinion on game design of a game They're playing design that makes that game enjoyable or not doesn't matter that they have to go make games to get it No, they don't if they enjoy the game as it is They like this feature in the game by nature that makes it good game design for them But what am I gonna do?  I Respect the work you've done in this industry. I just wish you would respect the fans that you make the games for Their opinions matter because this is their entertainment medium not yours You make the games for them to enjoy if they enjoy it Ergo, it was probably good design. It's fine to be hyper critical Just don't insult people along the way and insult their intelligence and act like their opinions matter less than your own None of them matter more than my opinion doesn't matter more than yours and yours doesn't matter more than mine Can't we just leave it at that?  All right. So here's the thing We knew there was something going on between FIFA and EA for a little bit now because EA publicly announced that They are looking into rebranding FIFA moving forward. We weren't really sure why we just assumed Oh bad FIFA FIFA wants double the money. That was the report FIFA wants double the money Bad on FIFA boo FIFA boo the money-grubbing multi-billion-dollar organization, which also applies to EA boo on them Well FIFA came out and fired back and let's just say they are very upset with EA Here's what FIFA said FIFA is bullish and optimistic about its long-term future in gaming and the eSports following a comprehensive and Strategic assessment of the gaming and interactive entertainment market It's clear that this needs to be a space that is occupied by more than one party controlling all rights FIFA has also determined that the overlaps between virtual sport and FIFA's football Competitions must be more closely aligned in this respect FIFA is excited about using the FIFA World Cup with four billion viewers and the FIFA's Women's World Cup with an audience of 1.2 billion as platforms to launch integrate exciting new games and eSports Opportunities in other words. They are tired of being exclusive to EA Sounds like a broken record, doesn't it? Disney pulled exclusivity of Star Wars away from them. I hope the NFL does the same And pulls the exclusivity rights away from Madden. Now look, I'm not saying that I want the FIFA Games to stop from EA continue to make your soccer game You might have to call it something else now because FIFA doesn't want their brand to just mean one thing They want it used across multiple games, but you know how good this is for gamers? FIFA can now use its branding and everything else about it in multiple games of multiple companies This is going to lead to better competition in this market And by the way as switch owners, you know, exciting this is because EA has treated us as second-class citizens Since day one with FIFA keep getting legacy additions. Oh, no, this is the best mobile screw-off They're making an excuse to not give us current versions. Well, guess what now? It can be put in the PES It can put into other games that can now use the FIFA branding to help Inspire their games to be even better and they can become front-runners this hurts EA and it helps gamers and If you enjoy EA's brand of football games, that's fine as well But guess what they're gonna have to make them better now because their strangle hold on the FIFA IP the FIFA licensing is gone So now the players the likenesses the leagues the World Cup can all be present in other games You done messed up EA to the benefit of all of us So I guess thank you for that now this next story is about this video back here and It's ratio bad Nintendo's switch online expansion pass has 16,000 likes the 74,000 dislikes No, Nintendo's not gonna change the pricing. I know they're not gonna change it You know they're not gonna change it and there'll be millions of people that end up spending it anyways as we saw with the n64 Controller selling out like that, but here's the thing. I Don't really think the price point is worth it now Maybe you're someone who's just super looking forward to playing n64 games online and it didn't matter what they charged It was gonna feel worth it to you You'll see people in the comments telling you oh, it's just four dollars and X cents per month Oh if you have you know, if you're on the family pass It's only ten dollars a year That's assuming you have eight people and you're splitting the cost which most people with family passes do not it's I Get it right in the grand scheme $50 or $80 isn't a lot of money. I can't even fill the gas tank in my car for $80, right? So it's not a ton of money to spend once a year No one's arguing that the total amount of money is a lot in the grand scheme a hundred eighty dollars for X-Farx live gold plus game pass. So basically game pass ultimate for a year Isn't really that much money either But it's not about whether or not it's relatively a lot of money compared to putting gas in your car buying Groceries paying your electric bill. It's not about whether it's a lot of money in comparison to that It's about is it a lot of money in comparison to what everyone else in this space is doing and yeah You aren't giving us basic online features and you want to start charging very close to what Xbox and PlayStation charge for a year without Giving us modern games every month or the guarantee of even adding games and 64 and Genesis every month There's no guarantee that's gonna happen There's no guarantee that you're gonna actually do anything with this improved online infrastructure. You supposedly have There's no guarantees of anything getting better Voice chat localized on the platform adding the ability to message people There's no guarantee you're going to add any of this So because of that people are upset You're charging a lot of money relative to other companies and not giving us as much value and that's ultimately What you're gonna get your criticism. So it is what it is Nintendo ain't gonna change But I hope they take this feedback At least not just the dislikes but the feedback in general that people are talking about why they dislike this price and Take it into consideration when they make new additions to Nintendo switch online in the future That hopefully exist doesn't we don't know if they're gonna make any new additions or this last story is just kind of a quick one So Sora came out in Super Smash Bros. Sora from Kingdom Hearts released last night in Super Smash Bros Crash the e-shop all that jazz it happens, right? Every time a really popular Smash character comes out There's a little e-shop snuffoo for I don't know an hour or so, but here's the thing Cool Sora's out. Everyone's loving them playing them enjoying Smash Bros. But you know what also happened Super Smash Bros. Ultimate hop backed up into the top Three on the Nintendo switch e-shop showing that Sora from Kingdom Hearts is selling Super Smash Bros copies. It has been a long time over a year Since Super Smash Bros. Ultimate has been in the top three on the Nintendo switch e-shop here in the United States It's a rarity for that game to get back up that high on the charts But Sora made it happen This also makes you wonder how many people who bought Smash Bros. Are going to sell the Kingdom Hearts collection coming to switch Which again, I think this is going to be impacted by the fact that it's streaming only so I'm not even sure it's a fair thing But we'll see maybe Smash Bros will force people or not really force but encourage people to actually massively buy Kingdom Hearts on switch even if it's only streaming Time will tell but clearly Sora was obviously the right choice financially for everyone involved Square needs to be ecstatic about the attention that Sora is getting and Nintendo's got to be ecstatic that this final character is selling so many extra copies. Maybe millions Also, by the way, Metroid Dreads still at number one. That's pretty badass Alright folks, that's gonna do it for today's video. Thank you so much for tuning in and I'll catch you in the next one


I'm David Duncan. I'm a partner solutions architecture manager, which means I'm on an alliance team and I spend a lot of time working on Red Hat Solutions. Could you just look to the right? Sure. How are you? Yeah, that's a sweet spot. Sorry. David Duncan, partner solutions architect. I work as a manager now, I guess. And I've been working on Red Hat Solutions on top of AWS for the last eight years. And before that, I've worked with Red Hat as a software development engineer. Surprise. I wanted to talk a little bit about machine learning ops. Not because I want to spend a whole lot of time teaching you about ops, because I think that there's a fair amount of understanding of machine learning and operations at Red Hat. I think almost every engineer here is participating in operations space. They're doing testing, they're doing build and test methodologies almost every day. And I think that there are very important key understandings that we have in design that are important for success. Like I said, I am an architect and that's going to become painfully clear in the discussion points here. And I kind of put that up on the slides here to identify where it is that you're going to see me. We're going to see my talk and we're going to see the things that are associated here is that I put a lot of time and effort into determining where there are target requirements. And then trying to figure out how we're going to build products technology to match those. But I do that in the context of the cloud. And I think that the cloud is a very important part of machine learning. We've all been inundated with conversations around generative AI, or at least I have. And I think my team has been sick of hearing the words generative AI twice in one day. And I wanted to talk a lot more about designing for success and what key components are there and how to kind of isolate what needs to be done. And maybe this will help you as engineers or data scientists or machine learning engineers or practitioners to find a way to structure your ops and structure the configuration. And then look at some of the tools that are available to you. I'll get into this in a little bit, but my experience has been one in which that design has led to product designation. And so I'm spending a lot of time talking about where those products landed and a little bit about how the development cycles and what made that go. But at Amazon there's a practice we call well architected. And I was part of the team that designed this as well. But it's a big team and this doesn't look very clearly this is not a one person job. So in early days we were just trying to figure out how we would do five pillars. We had four pillars when we started. We got five. Now there are six. Super exciting continues to grow. But I'm constantly looking at where things focus in terms of operational excellence, in terms of cost, in terms of sustainability. How all of that effort comes together and where it comes through. And what we do at Amazon that I really like and I'm going to get to some of the things that I really like about the design principles that Red Hat too. So in that open source community, especially around open infrastructure, which we have some people represent here at the front. But there is what we call the lens. And the lens is a way of actually taking those the challenges that you face in any one particular component of infrastructure development or your line of business. And really focusing down on the core decision practices that make that happen. On the Red Hat side, what I really enjoy and what I've talked about a lot with other people who are involved in this, some of them who were on stage this morning for the keynote like Steph and Brad are things like architectural decision records. And creating that infrastructure as code or the decision as code with a certain amount of historicity. An important component of what it is that you do in your operations. If you don't have that, then you're doing to repeat those same those same decisions, right? And that is a very complex and complicated problem. So sorry, I thought I had a little bit of a pathway. But so wherever you land in the industry, like if you're in gaming, if you're doing large scale databases, point of sale, wherever you're collecting information from, pulling information from doing whatever it is that you want to do, whether that's like some sort of really basic recommendation engine, or you need to do some sort of highly structured publicist groups style investigation unstructured data that may take you in whatever direction. There are lots of places to start. I personally started on the very far side of there in the HPC lens. And the HPC lens was a place where we created a structure for things that was very siloed, right? So you have a front end node, that front end node would communicate simple chunks of data out to a large scale group of nodes that were there to just crunch that data and return it to the front end node. And we don't do that in machine learning, right? Machine learning is typically done in the same way that we do Hadoop, or in a way that is opportunistic now. And the opportunistic modes are some of the things that I think are really, really interesting, and what it is that makes this happen, you know, what makes me excited about it. I just want to talk about it outside of my team. So as you build that structure, as you build that pathway, each one of these ends up having a lens, right? You end up having a way to do this. In Red Hat, you have a solutions collection, and each one of those solutions collections is pretty easy to sort of review. And there are people here doing that research and people who are actually doing research in university who are contributing to those in an open source way that I highly recommend that you spend some time reading, right? And taking some actors there. And then also there's open infrastructure that you can participate in today to experiment and learn and build out a structure of practice that's similar to what we were talking about, or what was being spoken about this morning in the keynote, which is that open source software as a service. And machine learning itself has a lot of component parts that need to be extremely well documented, well understood. And part of that is tools, part of that is finding out what it is that we're doing in our documentation. And then some of it is just basically the paperwork, right, to get things done. But ultimately it comes together in an operations model. This is sort of the crux of what I think is really important for us is that we have to figure out a way to ensure that we have a data processing model, and then we need to ensure that we have a way to build a continuous cycle of this. I guess I could have done this in a circle, but I didn't. I mean, and really the focus that we have is kind of starts for machine learning ops, kind of starts at collect data. These other two components are a really nice thing to have, but they don't necessarily fall straight into the operational models. They usually, this is what you get handed as an engineer and want to continue forward with. So I stuck with, you know, I stuck with leaving that out. We're not going to talk about business. I can't do it. But I can talk about what I think is the continuous cycle here, right? So I guess I did make a circle, and I put it on this slide, not the one before it. Each one of these steps that goes into this process has to be clearly defined, and this is something that I think is really important is that we find the parts of this structure in each one of the phases of the business so that you can, so that you can clearly define them. So I put in here that we were talking about this all the way to the edge. I kind of thought about it backwards, which was all the way from visualization, and all the way the edge meant trying to find ways to tune back into whatever it is that you're evaluating, whether that's the hum of a giant tank of oil or the position of a series of ships. It doesn't really matter. Each one of these component parts is really important. Anomaly detection is one of the things that I first worked on, and I think is a really important part of that edge computing process, like how do I get this data? Over here, which data do I transfer? How important is it? What is the clear path for doing that? So thinking about things from this perspective and listening to customers talk to me about this, I spent a lot of time trying to determine what the first component of this was going to be. So a lot of that just came from determining that models, most people were just looking for a model. They weren't thinking to themselves, oh, I'm going to go make a model. I got one, and I want to really implement this. If you don't know what it does, and you just need to have something that works, you've got to figure that out in this process. This is messy. This is not exactly where you want to have... This is not where you're doing ops. This is where you're trying to figure out whether or not you have a structure. This is essentially the first part of your machine learning experience, and not where you're thinking about how you're going to properly tune this configuration. Good news, once you've done this a couple of times, you'll start to have an operational phase, and this part will look a lot like that little black dot in the middle, and you'll have an understanding of how it is that you're going to create all of these essential silos in which you can now start to create your infrastructure model, and then your CI CD pipelines and how that configuration comes together is really important. And then this first phase, the pilot phase, becomes a lot less messy. So for each one of the next iterations that you have, now you have this machine learning ops kind of model, and this is where you've already got the decision records. You already have all these things in place, and you can start to push them into play. This is the great part about this conversation that we're having here, is that when you look at how much of this effort actually goes into production, and these are kind of little numbers, but they're still consistent. I did a little background checking, and you're like, yeah, these are still good. You're still not making a whole lot of decisions. You're doing a lot of transitioning from one model to the next to try and determine exactly what it is that you're going to get out of it. And I have lots of fun stories about making those decisions, making changes in model, retraining that model, and then determining whether or not the aspects of that determination were in fact valid. And explainability is one of those things that is almost guaranteed that you will have a lot of trouble in this space if you do not have already a process and pipeline that makes it work. I started off thinking about this in the context of this, and I'm highly opinionated, so I'm just going to tell you right now, take this with a grain of salt.  Because I started from the simple fact that I work on a team where the center of my universe is actually open shift.  Making this a product and turning open shift into a service on AWS was started off by creating incremental changes that people could use in the context of previous versions of open shift. This didn't appear for us until four, but on three I wrote a scale out model because the problem with the operations for building out a structure for cloud on an open shift was that no one had thought about how the scale would work. So scaling out was super easy, you just buy another node and that was a fun part of that experience. But the goal was, I thought, was to scale in. I'm not using it today, so how far can I scale? My favorite support question that I got from that, the emergency support call that you get on GitHub, was from a customer who said, I've scaled down to one node and I don't seem to be able to scale back up. That is true, STD is not going to let you do that anymore. And I'm sorry. But good news, you have a cloud formation template that will deploy it all over again and you don't have to create the auto-scaling groups that are allowing you to do that scale in and scale out. We did that, so the structure of that, the design we did was to create an auto-scaling hook configuration and the auto-scaling hook configuration would do all of the node setup and then when you wanted to scale down, the auto-scaling hooks allowed you to tear down the node in a proper way in the old OpenShift container platform model. And so this was the start of getting those people who were making the business decisions very excited about the fact that they had an incorporated model. And so as a foundational layer, that was the thing that created the excitement that made two business decisions, one on the Red Hat side and one on the AWS side, that brought us a service that would in fact support doing a lot of that work. Now, everywhere you go, that product design started to become a really important part of how it is that we build this concept of two platforms talking to each other and in fact, integrating together. Being integrated together means that you have to solve other problems on top of the problems that you have for just creating a container-based platform. We have many different selection, so now there's many different ways to deploy OpenShift on AWS now.  And then you've got these other things that you can do which are the vanilla options, what am I getting myself into? Oh my gosh, now I've got a practice that has to make all these decisions. And that's where the decision records come in. You can go back through those, you can make a decision, and then you can make another one based on that. In fact, there's some in the blueprints for the open infrastructure that talk about some of those failures and the failed decisions or the advantage of one option over another in terms of like making a decision about one open-source project or a platform component over another. And then relating those back to best practice, that's what those that well architected ends are for and the components that you find in there start to force, it's a forcing function to ask questions like is this sustainable? Does this apply operational excellence? Am I getting scalability that I expected to get? So using those techniques and the technologies that are associated with that kind of question-answer experience is a great way to form that audience. So take that, take those six phases and the operations and then look at how you're going to put that together. And you'll start to see that there are some places, there are going to be some big holes. So you need an inventory, right? You need an operational inventory and so once I looked in and said, we've got roses life on the way, what else are we going to need? Well, it turns out that you need a way to build out this infrastructure that is consistent with your expectations and I chose Ansible. So an obvious choice, right?  But Ansible is also, you know, my personal favorite. I can make the decision for that outside of my experience, right? But there are some things that I think are important and they are supported by creating enthusiasm for that same kind of technology. So identifying that that was something that was really important made me continue to talk to our cloud formation team and say, hey, I really like what I can do with Ansible and I don't like writing this j-time target and then not being able to iterate very well or find ways to do more minimal tests. And that team decided that they didn't like having just one way to do it and that they wanted to be able to incorporate that strategy into what they were doing and so that's how the Cloud Control API was born is out of that iteration, out of us talking back and forth and, you know, that didn't just happen to Ansible, it happened because tools were changing, right? Chef was going away, puppet's going away, nobody wants to use those anymore. The server model doesn't work for us and it certainly doesn't work for machine learning and I'll get more into that through this, you know, because we were looking for kind of an easy way to get, to do the discovery for the resources to create a way of building out those best practices in an iterative model and also in a way that was easily consumable. So Ansible becomes kind of an easy place for us all to create opinionated decisions. On the other hand, it travels well so I don't need, you know, this is just one aspect of it and yes, this is David's opinionated inventory, I'm not, you know, when you get out to the edge you're going to use different collections, you're going to use a different experience, there's going to be a different experience but it's all going to be part of your execution environment and support infrastructure is covered. So originally there was nothing on AWS except the Red Hat Enterprise Linux. In fact, that was the only thing that ran on Amazon originally. In 2006, you could get a Red Hat box, right? That was it. And once we started to look at what it was that we were doing with data there were a lot of places for us to go with that and some of the other things that became kind of obvious was well, we still need high availability, right? High availability configurations were absolutely necessary and we still need a way to manage data at the edge. There are lots of times where you're collecting a lot of information and you need to put that information into a large scale data lake and we found that there were some options that Microsoft SQL allowed us to use in collecting that data at the edge, right? It made it very easy for us to have simple communications over VPN to collect small amounts of data and then pass that data into buckets that were associated with long-term storage. And that has driven a lot of variation in the way that we birth those. So looking at kind of an example of how we build a pipeline we start with code, that includes the infrastructure as code and the open shift kind of stays in the middle. In the early days of machine learning from data scientists and things like that people who were actually crunching the actual algorithms and trying to determine whether or not they were fully functional we have this tool that they call Deep Learning Ami and the Deep Learning Ami was exactly what I didn't want it to be. But it works. It seems to work. Everybody loves it. I think it was a collection of basically every scientific library that you can think of for Python, Ruby and R that's just like dumped into one machine image with all of the NVIDIA crew new drivers and everything and then you can just basically leverage that for component parts. But that doesn't make a pipeline, right? And so in this case, you know, this is outside of the pilot phase these are kind of the things that you end up doing to get structured data. And this represents for me a data practitioner. So if you're looking at the way that this works you'll see that I've got up in the far right corner the concept of using Amazon Athena which is a way of building out a SQL style analysis of the contents of an S3 bucket. So if you have structured data or mildly structured data you can create a configuration and get something back. And then AWS Glue is a way to create tokens and to tag data so that it goes from an unstructured to a more structured model. And you can get those artifacts and get the trained model from that experience and then you can push that into production. So once you've got your approval process to kind of transfer that over on the top once you have an approval process you can move that over into your production cluster that cluster can come with you. And then, or I'm sorry, the artifacts can come with you your trained model can come with you. You can pull the batch inference production data from there and then use that model in a way that is consistent with your business requirements, your recommendation engine or whatever. So that whole process is exactly what I think of when we bring this out in experience and that's why there are tons of tools for doing this. There's a single-node outpost is an edge solution that we can use. We can use that in the context of Greengrass you can impact for the AWS experience. We have a small method for doing basically circular functions at the edge and you can collect that information and stick it into S3. And then this becomes, this is the simplified way. As you become more, you know, as your data engineer who finds this more and more you may find that the basic tools no longer work and you'll create a more bespoke model that is consistent with building out on the Rosa experience where the open data hub tools will take you through that whole process. So that effectively is everything I have to talk about today and then to say that this is a great opportunity to talk about machine learning ops and what makes this work and to see a little bit of an example of from an architecture point of view what it looks like. Does anybody have any questions? You briefly mentioned monitoring and model drift so in your experience how difficult was it to set up both the monitoring part and the model drift like dealing with the tree training and how difficult was the whole cycle for you? Well, so for me the, I mean model drift is less of a concern because obviously I'm more focused on operations than on the models themselves but what I see from my friends is that because the training is happening so consistently when you put this into operation like if you've got the Ansible models that are like building up the systems and then you're doing your basic training and then you've got that model in connection with your data and I didn't mention public data sets but public data sets are like a huge help in terms of training and they come across as an S3 bucket in fact quite commonly I would use those in the context of OpenShift so I'll just grab that and then just put the assignment for the public data set in an open data hub workspace so those like building out the YAML structure for that and making it work reduces the amount of effort necessary for retraining right and so that's what I was talking about like the pilot phase is messy and you will, there will be much gnashing of teeth while you try to push that back together which is again, I don't like that machine learning, I mean even though it's like, it's incredibly reproducible because you just pick one and you just use it over and over again but you know if there's a security vulnerability or something like that or if there's a modification that you really want to take advantage of then you have to switch your entire environment Any other questions? This session is still running I'm sorry Modern serving is obviously a big part of operating models on the inference part a couple of years back there was a myriad of young projects trying to solve that starting from just wrapping in a class application and then deployed as a container that TF serve and some other projects trying to solve that I wonder if we now are converging to a more cohesive consistent deployment model when it comes to inference part and maybe what is Amazon doing on their back ends or their AI services is it always just homegrown depending on the use case or is there some convergence I think there's a good, well so there is always an opinionated convergence so if you look at Red Hat I'll start right there and if you look at how Red Hat does this in the other data hub right you have all the tools that are associated there and then you have a way to just generate you know, I mean the standard on structures is to support the pilot phase in Jupyter notebooks and then create that configuration and maintain those models in specific containers so that you can have that container space but I don't know that Amazon the way to do that is they create SageMaker they use some very opinionated models and they say you Mr. Customer can use all of these exactly the way that they exist today or you can maybe later make a decision on how you're going to use it like, you know, use a different one interchange them but in my opinion, you know part of what we did like building a rail workstation on top of AWS was to create spaces where if you knew what you wanted to do in that messy phase you could have immediate access to the right kind of hardware from a Red Hat instance where you would know about it and create an open source model where, you know, you might want to use it I think we're out of time One more minute Oh okay, one more minute Another question? So you do mention that I'm a double station worker so I've been using a double station worker for a while but I wonder is primarily intent for training models but do you have any sort of Well, okay, so talking about it from the perspective of the Amazon products so SageMaker I think is really meant to leverage models that are there and then you have the ability to train, you know to do some additional training on some of the exit models and then you can add your own if you want to in that process there is what they call machine learning ops which is basically a service or machine learning ops that is available there for me, obviously that's not where my work extends into how I can make this work on the Red Hat Open Data Science model which kind of gives you the same similar kind of environment but done in a very open source play in a way that is achievable on other platforms so if you have a hybrid scenario and you want to have the same experience from your on-premises to your public file experience then I would say you can do that in the context of Open Data Science or Open Data Hub and the SageMaker would give you sort of similar tools in that sense if I wanted to go build a cluster and then use Dask to handle a complex problem across a suite of systems I can do that as easily in the context of an OpenShift cluster on AWS as I can just building that out in terms of machine learning with SageMaker and the SageMaker networks well it was a very nice talk and if you have any further questions I think you would be more than you know, glad to have them I'd be excited to talk about it, yeah talk about it all day


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What is going on guys welcome back to the channel and I appreciate you being here today I bring this destiny to top five WTF moments of the week Remember people if you do enjoy the video leaving a like really does help me and my channel out And I do appreciate that support as of late, but let's get straight into this top five in at number five We have a clip sent in by Prince Alfraser. I think that says literally a pixel in it It just goes to show how tight bungee are Are you serious? Are you DPJ? This is going to DPJ. No, it's going to DPJ What's going to DPJ? No No No What oh my god Yo, what the In at number four, we have a clip sent in by choir Um Wow I just don't know We have a clip sent in by debut on the left. Did he just fall over? What the flip happened here? Been heading your way Then hurry up with that booster. Let's finish hooking up the calm network and uh We have a clip sent in by shadow power showing us this epic full run of the war beast within the level fan And managing to do one million damage ridiculous Clip sent in by kenyalj92 Who shows us how to catch your own overbump epic Guys as I end this top five wtf moments of the week Remember if you have any amazing clip to send in send it into the email I just linked within the top of the video description But thanks as always for stopping by and I'll catch you on that next one


Today I'm talking about one of the saddest planets in the solar system. It's not Mercury who just lost one of its space probes messenger and is now a bit lonely. A lot of people would say it's Pluto because it got demoted to being a dwarf planet and it hasn't had anyone visiting it yet, although the New Horizons probe is coming to see it this summer. To me, the saddest planet in the solar system is Uranus. And it's probably exactly for that reason. You're probably laughing at it right now. You're probably giggling at it and making up silly jokes about it. Uranus is actually a really cool planet and it's completely the underdog of our solar system. Because people always make jokes around it constantly, it actually could be keeping kids from asking questions about the planet. It could keep people from studying the planet. It might mean people avoid learning anything about the planet at all because they can't get around the giggle factor. A lot of people think that since it doesn't have anything like craters to look at or amazing clouds like on Jupiter, that it's a fairly boring planet. And this is just so heartbreaking. While people are busy giggling about it, laughing at it and making fun of this poor little planet or I guess in this case a large planet, it's really freaking cool. It actually is a sideways planet, meaning that its actual equator is perpendicular to its orbit. So it actually rotates on its side in a sense. And this makes Uranus actually really unique. People have theorized that this is probably due to a collision with another planet at some point in its lifetime, but that hasn't been confirmed just yet. What's also freaking cool about Uranus is that it's an ice giant. 80% of the materials on Uranus are actually made up of icy materials. So Uranus is able to actually achieve the coldest temperature of a planet in the solar system at negative 224 degrees Celsius. Perhaps the coolest thing about Uranus is that it has a moon named Ariel, duh. I mean, how much cooler can you be than that? Well, I guess you could also have 27 moons total and some of those moons have ice canyons and that's pretty freaking cool as well. The thing that breaks my heart about people laughing at Uranus or calling it boring is that we're actually still discovering amazing things about it. In 1977, we discovered that Uranus actually has a ring structure around it. Perhaps even more exciting in 2012, the Keck telescope actually was able to discover that Uranus while it looks like a featureless planet is anything but. When we actually look at the planet in near infrared, we can actually see clouds and storms and all this exciting activity. In fact, in 2014, we were able to discover that Uranus was having a massive storm on the surface and we don't know exactly what's causing it. So hopefully some of you are space geeks like me and your hearts go out to Uranus because it's actually a really cool planet that people avoid learning about just because they want to giggle about it instead. Well, it's a lot of fun to make fun of space things and I totally get it in some weird middle school way. I wish more people would actually take a chance and actually learn more about this really awesome planet because we're still discovering amazing things about it and I think it deserves our love. Speaking of needing our love, feel free to go to the Patreon campaign to support space pods like this one and also subscribe on YouTube and leave a comment and I guess I'll see you guys next week.


Don't know what happened to stop recording. All right guys. I don't know if it stopped recording or what but I just noticed right now that it said Start recording, but it should have been recording this whole time So, um, that's BS. Well, anyways guys, I mean The whole stream video does save. I'm gonna see if I can try to save it in the background, but How's it going on here? Computer's actually really slow No, actually it's there for some reason it might have stopped I don't know if it stopped recording or what but good thing is that I haven't gone through the autos yet I guess that's the good thing. I did rip all the packs These were all the colors and I'll re-show everything again just to be safe But um, I guess I don't know what happened there I'm pretty sure I clicked record, but it might have stopped or something Which is weird. Did we run out of memory? I don't think we run out of memory. That'd be weird Anyway guys, sorry about that. I mean, you might find this video a little bit shorter so you didn't really see me rip the packs in the boxes, but of course this is the case and These were the hits that we pulled out of your orange Right there. Young Carl Stan, Shane Baz, large news for our blue, Andrew Vaughn, Green, Yankees Louis Gill, Vidal Brujan, Tampa Bay Rays, Minotendi, Barr, Suzuki and just in case my video for some reason didn't record I'm pretty sure I clicked record. Let me show you the names and teams there That's so weird. That hasn't happened to me in a very long time, but I'm pretty sure I clicked record Yeah, it was gremlins, right? Anyway, I haven't done the autos yet. So that's the good thing So we're about to start. I usually rip all the box to stack them up just like on all the previous videos So I only did the packs. Here we go. It is recording now. So at least you count that All right first one Colton Welker for the Rockies Rockies that is Greg And we got a Ryan Veladi to 99 that's for the Colorado Rockies as well another one for Greg back-to-back hits Rossny Contreras for the Pirates Pittsburgh Pirates Joe Christian And how about more Pirates again? What is this back-to-back hits for the same teams 20 out of 99 Hill Park? What's going on here? Josh Lowe for the Tampa Bay Rays that's going to Javine. This is a Tampa Bay area. It's gonna be wild No, it's Steve Garry for the Dodgers though That's Jason Kay Reed Detmer's for the Angels Louise Gill for the Yankees We got Jake Burger for the White Sox. That's Joe Christian Another one for the White Sox Gavin Sheets to 150 this time. So we get a lot of dupes back-to-back My two more Paul Goldsman to 99 for the St. Louis Cardinals Steve or Stephen Carney with that one And last hit of the break guys here we go For the Arizona Dive X Seth Beer going to Christopher C So there you go folks. I'm gonna try to get to the bottom of this. I don't know what the hell happened there, but Said I I've recorded this whole time. I don't know why it stopped for some reason, but Anyway, I appreciate you guys. This was the top chrome black I'll try to see it might be a two-parter guys just in case it had stopped out of nowhere But if not at least I got the main stuff right here and like I said This is number 26 number 27s in the store Jasper's case breaks.com


Can you hear me? Okay. Hello everyone, I'm Lorenzo Grande from ID TechX and I'm here with Dr. Elia, who is a senior researcher at the Technical University of Berlin, and he focuses on unconventional battery chemistry. Is that right? Yeah. We actually are focusing on the development of an aluminum battery system and trying to find a way to overcome the issue of the possibility of the limitation of the lithium resource. In fact, considering the chance to have a complete electric fulfillment of the market by electric vehicles, there are some prospective that say that the lithium will be not enough. So we have the problem to find the alternative electrochemical system using much more abundant elements and that can be sodium, magnesium, potassium and aluminum. In particular, our activities focus on aluminum since it's the most abundant metal on the earth crust due to its three valencies characterized by the really elevated energy density and it has a volumetric energy density that exceeds four times the one of lithium system. Wow, that's amazing. That will be a great advantage. Clearly, the system is still in this infancy. The real development of such system starts more or less one years and a half ago. So we are really, really at the beginning, but there are a lot of space to make improvement and to try develop some good systems. Yes, and your lab was one of the first in the world to start working on this chemistry. Yeah, actually the first research comes from the very famous Stanford paper in nature about the aluminum battery system. They clearly demonstrated that it's possible to make a battery out of aluminum that is unbelievable because it's a really stable metal. We use it to pack the food and use it in a battery to make a reversible electrochemical process. It's something that is really particular and but now we found the condition to make it possible and we need to improve it to make it bring it to the real market. Do you have one? Great. Not yet. Not yet. Not yet. In three years, four years, maybe. Okay, and in terms of energy density and average voltage, what numbers do you disclose? The point is that clearly the voltage of the aluminum system is much lower and it's a 2.5 volt lower respect of lithium. Basically we cannot believe to beat lithium in terms of energy density. Okay. There are some space to try to reach lithium in terms of volumetric energy density since we can one of the main advantages that for this system you can use directly aluminum metal. That is not possible for a lithium system. So in the development of a real battery pack this will be a great advantage because we'll be enough a few microns of aluminum foil to sustain the capacity of cathode material. The problem is the cathode material that the only one that is suitable until now is the graphite with a limited capacity that is one-third of the conventional lithium-ion system that leads to the actual aluminum battery system that can be developed. It has an energy density in the range of the nickel metal hydride batteries. Okay, and you will be talking about your research this afternoon in the Track 7. Yeah, actually I'm involved in a European funded project. It's a lion with the work that involves several industrial partner research institutes and we are trying we are we have some collaboration also with the Helmholtz Institute in Berlin to make some more research on this topic. So we are quite confident that in the following years we will be able to find something that is good. Wow, this is great. And are you enjoying the ID TechLex show so far? Yes, the first time and it's really great, really huge. The exposition is really impressive. Also they talk as well. And the food is really good. Okay, we're very glad to have you here. Okay, thanks a lot.