Good morning. Good afternoon. Good evening wherever you're hailing from. Welcome to a very special virtual Friday edition of the data services office hour. I am joined by my fellow Red Hatter's Carl Eklund, Sophie Watson and young. Yeah. Yeah, yeah, yeah. Yeah, you got it. All right. I'm winning this morning. All right. Kind of had a rough night here in the short household, multiple thunderstorms, lots of lots of child holding and such. But we are here and we are ready to party, I guess. So, topic of the door is data engineering and data science and data engineering, like that to the layman here. I'll fully admit it. I'm no data nerd. Kind of means the same thing to me, but doesn't. Right. Like I get it. There's CS. There's the science behind it. Then there's the engineering behind it, like the application of it. But with data, how does that like slice, I guess, as I hit my mic. Don't all jump at once. I can start if you want, because I'm old. So that means I've seen those definitions change over the past few years. So maybe we can start with that. It will help people figure out what's what and how do things are settling. Yep. Everybody introduce yourselves first. Yeah, sorry. I forgot to do that. It's early. I haven't had coffee yet. But please, everybody introduce yourself. Yeah, yeah, since I have the mic. Okay. Data engineering architect in the, in the, the cloud services be you so the title is not interesting. What's most interesting is what I do is I try to help organizations, various organizations to set up their data science platforms, which includes that engineering, the data engineering box. So I'm here to try those people figure out the infrastructure, the applications that they have to put up even some aspects of the project, how they want to run it and helping them sort this out. That's what I do. Awesome. Well, I'm Carl Eklund. I am also an architect here at Red Hat. I have a background in academic numerical analysis. I mean, I don't don't always call that data science, but there's certainly data science happening in academia. Right. The specific field I was focused on it wasn't quite data science, but that's where I gained all of my interest and then I moved into private sector and focused on technology and data strategies for enterprise surrounding data science and you know actually getting a return on your data science investment. So it's kind of where I'm coming from. Awesome. And I'm Sophie Watson. I am not a data engineer. I'm a data scientist. So I'm really excited today to hear from the experts about how they work hard to make my life easier. I just want the data. I'm kind of with you, Chris, and that kind of Right. Data just happens. Data just arrives and exists and I don't really think about how it got there. I just want to use it to, you know, iterate through my workflow and not think about what they do except for this hour where we're going to learn about what they do. Wonderful. Okay. Yeah. Yeah. And I think it's a nice introduction into introducing ourselves is a nice introduction to this discussion because you see we have different backgrounds. I have these backgrounds in infrastructure and you know general architecture for foreign organizations. And that was the city of level university here in Canada so also in the academics, but not from a pure data science perspective you know all the other systems that you have to set up to sustain 45,000 students university so which is much closer to a big enterprise then than what is done inside the labs inside the research labs at the university so I have this background, call is purely academics, originally but then shifted to the enterprise world to to to make this happening for really into organizations. Sophie, well she characterizes characterizes herself as a pure data scientist, but I know she's doing some data engineering. And if she don't want to issue. She doesn't want to that's that that's exactly the point she met which is really important. But still, we are at some point you have to do that and that engineering so what's about it. If I go back to what I was, I was beginning to explain a few minutes ago. When I started working on those things related to data science. So, let's say, back four or five years ago. There was this common knowledge that data scientists did not exist. It was described as a unicorn, because to be a data scientist, you had to new mathematics advanced mathematics, algorithmics and advanced statistics. Okay, this part of academia related area, you know to understand the algorithms, especially at the beginning of this deep neural network and those kind of stuff where if you have not done eight years of mathematics at the college, you will have to struggle. Maybe not understanding but explaining or using it to it can be hard.  And you also had to know about the business itself, because if you don't understand what you are trying to achieve. Most of times you will prove something that the business already know, because, you know, and it's true. It's the, it's the first thing normally you learn is that if you are trying to prove something that the organization already knows you're just wasting your time. So, there is a business goal when you, which may be differs from academia in academia you will try to prove that your, that your algorithm that your technique is working is producing interesting results. Okay, but in an enterprise, those results as have to be useful, which is totally different. You can find something that is fantastic but there's no application whatsoever. So, there's this differentiation. So, back to my story four or five years ago, data scientist did not exist. But you could build a data science team, you know, bringing all those skillsets together. Okay, some people are more versed into infrastructure and computing some more in mathematics and everything and the rest and business and you have this data scientist team. For whatever reason, in my opinion, partly marketing partly HR partly it's cool. The definition has shifted a little bit. And now data scientist will the common expectation will be more focused on these science aspects, algorithmics, and you know all those tensor flow by torch algorithm that you would train that you would use to train a model to recognize images patterns and data and whatsoever. That's what now that that's what would be the common description for a data scientist. But still, the rest of the work has to be done. That is gathering this data, storing the data, transforming the data, cleaning it, securing it, making it available, you know, all those. Yeah, not so interesting stuff that you have to do but still that that exists, and therefore came the name to describe this, the data engineer. That's what the data engineer would do. It's not only the data engineer because after, let's say you have trained a model, you have to put it into production. That's another skill set, which is now calling a software engineer or ML engineer machine learning engineer, you know, using those models so we have seen those. Maybe it's, you know, a refinement process. We started from, okay, general name for this new role, this new science that that emerged. And now, because it's still young, you know, all of this is really young to three years top. Now, we are seeing emerge those different categories with different skill sets and everything. And I have here maybe I can share this very quickly. I have this image and not even sure that's true. It's meant to challenge. Okay, it's part of presentation about, okay, as as the data scientist vanished. So it's, it's more of a challenging blog post than anything else, but it shows those different aspects of that engineering software engineering research and data business analysis. So you see different aspects of this of the work that you have to do around this. And if I take this example. It's lift the company, the car sharing company, they've made this change back in 2018, 19, and sorry if I'm wrong if not the right company, but for example, what they, the position that they named data scientists back around those areas, 2018 now is called research scientists. And people who were coined as data analysts are now called data scientists. That's just to say that those definitions are still floating and quickly evolving. So it's much more interesting to focus on, not the name of the role but what's being done, right, which is data engineering so all the things I explained, I began to, I began to explain about data processing, storing and, and all those kind of things. That's my personal view of the story, but I let go and Sophie came in and because I'm sure they have another perspective on this. Yeah, that was, that was an awesome intro. I can't. Yeah, yeah, no, I can't find anything in there that I would disagree with. I think one of the, the ways that I like to start conversations like this is through a story or just like personal experience. Like years ago now probably like five or six I was working with a not for profit institution, and we were trying to figure out the prospective donors right so how can we start to, you know, not only regress and predict how much a donor would offer but how do we take limited resources and our, you know, our staff to, like, go meet with these people and, you know, talk about like the donations and you know where do we apply our resources, right, to perspectives. What we found was as we went through this data science process and as I looked at the data and tried to understand it and build a model and do everything with it came to the conclusion that I just needed more data. I mean anyone who spends time around a data scientist is going to hear those words like the models promising, but I need more data. And I stopped for a second I was like wait a second. If I had more data, in other words, donations or transactions. I wouldn't need to build a model so it was like, you know, it seems kind of trivial but at that point in my career pretty you know, early on it was like kind of earth shattering and then I started doing the research and I figured, like, Oh, there's a whole data strategy component to this there's data engineering that needs to happen you can't just take more of something and expect the end result to simply be better right because in that case, more of that something would eliminate the need for modeling. So I shifted my perspective from just more data to high quality or different dimensions of data. And that's really where I view data engineering and how it fits into the puzzle. Cleaning sanitizing. I'll make sense to me right like, where do you put it? You just stick it in Spark. Do you throw it out of my SQL database.  Yeah, can we start with where does the data come from? Yeah, great. Where does it come from? From perspective. I guess it will be easier for the audience to follow. Of course, that's the first problem of the data engineer. So, you know, we have, okay, we have we're pretty agreed that the data scientists that take the output of everything that is happening before and they have a clean set of data that they can use. Okay, but before that, of course, you have to gather the data and that's what Carl explained that's what he faced at some point. Okay, where do I get this data? And how do I acquire it? So there's a whole set of solutions, software and techniques to help to gather this data could be simple as, you know, plugging a sensor to your environment and then okay, you are harvesting the data directly from the sensor. It can be web scrapping.  So, myriads of possibilities and each of them come with their own challenges. Do you want free time data? Do you want to do a batch every day you are doing this web scrapping? Or do you know every year? So that's part of the refreshing thing and that's part of the data engineering thing. But okay, let's say you have figured this out. Of course, what you have is raw data. So you have first to select what you want to use because from the sensors data that I took as an example, maybe there are four, five or five thousands entries that the sensor is measuring, but in fact, only two or three of them are really useful. The interesting part is that you may not know it from the start.  And in fact, it's the same data so you can just remove number 10. Oh, you have to go back to your pipeline and then no filter out number 10. And then another iteration. Oh, no, in fact, you know, number 10 was useful because it was a little bit different and so that's exactly what happens in the real world. And that's why it's, it dates back from, I guess it was a survey in 2016 but nobody challenged this so far so I guess it's the right number but 80% of the work you are doing in data science in general is spent exactly on this. Figuring out the data part, data cleaning, aggregation, ingestion or whatever. So you do this ingestion then you do the cleaning, okay, selecting the things. But of course you maybe have to group different inputs of data so you have some aggregation to do and refining. And finally, after some heavy work, you store it because of course you may... You've invested in this data at this point, you want to save it somewhere. Yeah, you want to save it and you want to save it for two different aspects to be able to work on it, making your inferences or things like that or to train your models. So two different, maybe two different sorts of data storage. There is some cold storage because maybe the data scientists, they will run their magic only next year but still they will want to have access to the past 10 years of data. And there is another data that you want real time to be able to do the inference and everything. So different techniques so far. So that's what we would call the data lake or it's the data lake. And yes, more and more we see these named as features because the data or features are points of interest that you can use for your model. So it's a feature store. We're evolving from this data lake thing. You put everything a row more or less and you figure this out to something more refined, which is a feature store where you have done the refining part. And then the data scientists are able to directly pick from this to do their stuff. So to your question about the tooling and everything, depending on the size of the data, you could you will have different, different solutions. Of course, if you run into big data, you know, a few terabytes if not pedabytes of data, you definitely have to use a distributed engines. All this one map reduce more common now spark or the new things like starburst galaxy, which new name for presto or tree node the upstream version, but those heavily distributed query engine that will help you make this sorting out and cleaning and everything because most of times it's it's lots of sequel queries, you know, it's not that different from from standard database but one interesting thing that I've seen is that people tend to use what they know. Okay, which is never bad thing. But sometimes it's good to you know to take a step back and think of something else. I've seen in a chat few days ago. Oh, yeah, I intend to use spark to do this. And, okay, but guy you've got 500 megabytes of data.  It's, well, it's, it's, it's kind of, you know, spark is great, but it's still some heavy lifting to have it tuned in the right way. And everything. So maybe just depend that data frame in Python is more than enough for what you want to do. And, well, data that the scientists won't like me, but I don't care. Most of times Excel is just perfect or any spreadsheet for what you do depending on what you want to do. So that's what differs maybe from academics versus enterprises and things like that. It's about finding the right tool where you will just choose what is necessary but no more. Otherwise you're spending on resources and time implementation. So if you do you do work in a, yeah, I was about to say, I called you a unicorn earlier, by the way, right? Like, he did, he did give you credit for being a unicorn, but Oh, well, no, that was, that was the data scientists of past that know how to do all of the things. I'm more modern, less unicorny version, I think. But I'm sure it's, it's, it's a virtual Friday all as well. So, again, when you're talking about the data engineering process there, and particularly kind of this movement towards using feature stores, and the process that you were talking about. So it sounded like, I mean, first off it sounded like there was a lot of interaction with the data scientists, a lot of back and forth to determine what the data scientists needed. And that kind of led me to wonder, I feel like you're talking perhaps about a specific type of data engineering when the data scientist knows what problem they have to solve. They've already been given their tasks, they know what they're trying to, you know, optimize for for the business. Is there a different approach when you're just, you've just got these sensors up everywhere. No one's actually using the data yet, because that's what usually happens, right? People just collect data for data's sake. Right. What would you do with that? Do you still call that data engineering if you're just dumping it somewhere, not looking at it at all? Is that a different role? Okay. Collecting the data is never a bad thing, because you're right, you never know what you can do with it. Okay, so that's what has been said to the organizations for the past few years, collect all your data. At some point, it will be useful. And I believe it's true, because with all the fantastic things happening in this domain every day. You know, it's not even every year, it's every day that there are new techniques, new algorithms and everything that's fantastic. So data can always be useful. Now for the part where, you know, you look at the data trying to find out something without any precise business goal. It comes down to business decisions. Okay, exactly like the data store. Okay, if you have the money, if you have the resources to store 10 petabytes of data a year, yeah, go ahead, do it. But it costs quite a lot of money. It's the same for data scientists. You know, they are unicorns, so they are really rare, so you have to pay them a lot. You know, not as much as it should be paid. Because you know, it's, no, I have most admiration, you know, I'm definitely not a data scientist and the mathematics behind this and everything it's it is baffling so I have a huge respect for for data scientists. But still, if you are paying them without any precise goal, at some point you will have the CEO looking, looking at the lines at the end of the year. Okay, I have hired those 10 data scientists cost me two millions this year, or whatever. What's the result. Exactly as, as a CEO, he would look to, okay, this factory that I've bought last year, because it was a bargain. Okay, what, what's the return of an investment zero. Okay, let's scrap it. Let's resell it or whatever. Unfortunately, in, you know, in the chats in the community, I've seen these beginning to happen. Business people beginning to challenge the usefulness of having a team of data scientists are okay that's perfect but what's in it for me. At the end of the day it has to be useful but I wouldn't say it was, in my opinion, a wrong business decision from the start. You don't start a project without any specific goal, or at least you call it a POC, and it has a defined, defined a timeframe. Okay, for the next two years, I will put a bunch of data scientists on it. If they find something useful, perfect. If not, it was an investment as a businessman, I will start maybe 10 projects like this a year, and out of them to will succeed to will be so so and the other six will be just crap. That's investment. That's one on one investment, and I'm definitely not a businessman so I won't pretend it's true, but I've managed many projects and, you know, some projects are successful, some are not. So, we are beginning to see these trends of people starting. Okay, but what am I doing exactly with this. So, to your point, so if it's your question. Yeah, it's important to gather and keep as many. Data as you can. But in my opinion, not starting with a precise, at least business objective. I want to increase my revenue by 10% or I want to have 10% more customers or I want to know more about them to target my marketing campaigns, or I want to improve the overall quality in my factory. I want only 1% of defective, defective products, instead of 10% that we have now. Maybe the data science part won't resolve the things but at least the data science team, they can they have a goal, they can focus on something. Okay, and, you know, it's, it's not that different from standard science in science. Well, it happens. It happens that serendipity gives you something that you were not looking for. But most of times you have a reason you have a, you have a project goal, you have a research goal. You want to cure cancer or generally speaking or I want to understand the interaction of this molecule with the sort of one you have a goal you just don't do just don't come in the morning and say oh okay let me mix some magnesium with some other thing and see what it does. So nobody does science like this. So you cannot expect data scientists to do the same thing in any organization. Oh, let's see what happens when I mix this data set with this data set. If you start like this, well, you're in for much disappointment. Yeah, because you've seen this before. Yeah, yeah, I mean what you're describing is taking a very logical rational scientific process and trying to just either, you know, use luck or pure chance or turn it into an art form and yes there is art as part of this but that comes from experience that comes from practice that doesn't come from just willful, you know, combining chemicals in a laboratory setting I mean I'm pretty that's very friend upon probably very, very dangerous as well. Yeah, don't mix magnesium is very dangerous and certain forms. Yeah, no like, yeah, yeah, basic science taught me all that like don't write random chemicals together. I think we need a PSA now don't mix chemicals at home people. But I want to go back to what we were talking about the, you know, poorly scoped business definitions and the lack of a return on investment in data science because this is something I hear all the time, I mean, all the time. I mean, we get paid did fly around the country talk about this sort of thing right. And to me it doesn't make sense and I think when we think of the diagram that Guillaume put up earlier and there was you know research and data science and data engineering and data analyst right that's the whole picture right and it's my personal opinion that a lot of people focus on the lack of a return on investment in just that data science aspect, because they're not considering all of the, you know, ancillary but required when we look at it in this view. Actions that need to be taken. Right and if you go back to thinking of you know my experience with low quality, you know, insufficient data, right, if we had a robust and funded data engineering process, and the models happen to not work out. The business is all the better for having a data foundation I mean that's what everything starts from is that data foundation. And so often, I would see groups say well let's just hire these people over here they're data scientists they're smart they can do something with that data because no one else in our organization has been able to yet. Well, that's not really the right approach so a lack of return on investment is possible. And not the numbers that we see out in the community right right. Yeah, and I think that that kind of notion that data scientists will save the world.  Yeah, it's, I mean, I don't need that kind of pressure but also. I think this goes back into you know what me and Chris Chase we're talking with you about Chris at the last session we had like in our last you know our we talked about how to integrate the work of data scientists into what application developers are doing. So, you know, I think we've seen these silos of data scientists that are off working in their own snowflake environments. And then when you want to take the artifacts that they've created be a model that they've trained in a Jupiter notebook, or a model that they trained in some Python file but you know when we're not, we're not application we don't have experience putting things into production. So when we go to put that into production it just kind of doesn't happen and the businesses aren't seeing the return. And kind of I think if we think about that machine learning workflow with the data scientists kind of here, and then application developers this some kind of communication and pass off there. I think with the data engineering, it's just at the other end and I don't know whether the monitor is going to be flipped as this gets streamed out to the world. So maybe my arm makes no sense at all right now, but it's kind of a timeline right and so I'm seeing kind of the data engineering is such a critical thing that is needed so that me and Chris can get to that point where we can put that model into production, because we need all of that data we need that communication. And if you take any one of these people out of this arm, then you're not going to have that nice workflow that lets you put these intelligent applications into production. That makes sense. And that comes back to your prior questions about the link between data engineering and data science. It's a teamwork. It has to be a teamwork, because at the end of the day, you can choose to spend a few months trying to refine your algorithm to have some more efficiency or whatever, or maybe you can throw into more CPUs and that's it, it works. So you see, or you can try to figure out, okay, why is my calculation taking so much time?  So all the things are totally intermixed. And you cannot take each and every part by itself saying, okay, no, data engineers, you prepare the data and that's it. Data scientists, you do the calculation and then software engineers, you put it into motion. No, it's a continuum. And all the PCs have to be integrated together because of the scenario I described at the beginning, you will have to do constant back loops to, okay, modify the data that you will ingest. And then it will, of course, modify your algorithm, then you have to reimplement into your production systems. So that's a perfect example of continuous development of integration, what we have been advocating for standard development for years. It's not that it's applicable to data science. It's compulsory for data science. That's how it works. Data is constantly changing. It's even more true compared to standard applications. Let's say you have developed something to manage your finance. Well, people will still, 10 years later, people will still enter spending or earnings and, okay, the application itself won't change much. That's why we still have tons of cobalt lines into those banking systems and everything. It doesn't change much. Of course, it has to be adapted to regulations and some evolutions. But it's totally different from this data science where those models have to be constantly retrained from fresh data or at least tested continuously compared to the data that is coming in and then redeployed into production. That's a huge difference versus standard standard application where I've seen in most projects you can, even if it's in Android mode, you have this waterfall process. Okay, this is the business requirements. It's fixed, finished. And then you do the development and then you deliver it into production. And that's over. It's not true for data science. It has to stay alive. It's a continuous project. And that's really challenging for any organization. Right. And on top of that, something I always think about with data science, which becomes more and more important is repeatability, traceability. So I need to, if I train a machine learning model, I need to be able to tell somebody at some point, some stakeholder or some customer why I declined their mortgage. I need to know what data this was trained on, what day it was trained on, what hardware it was trained on. Can I reproduce that result to like the bit level reproducibility so that we can go back and potentially advocate for the decisions that we made. Like, well, we made this decision now because of X, Y, and Z. And so for me that I need to know exactly where my data came from or what happened. And so I'm leaning on Guillaume and Carl to save the world. Yeah. Team effort here. Yeah, we all have capes. It's true. Nice. But Guillaume, you reminded me that I was an unfortunate data engineer at a company I used to work at because I worked with the data scientists to help them get their data where they wanted it to help them run their models against it. Like, I had to teach them a lot about building containers and about, you know, using the AWS, what was it Aurora, I think is what they were using. And like having to go in between that like, you know, DevOps to like, oh, you just need to store the data and clean the data mode was a very like hard shift. The skill sets didn't necessarily jive at the time many years ago. And, you know, the fact that I didn't realize what I was doing was actually data engineering was probably part of the problem. Right. Like, you know, it's just like how much like I just worry about spend at that point right how much money do you think is going to cost and like, you know, the business problems is what I'm worried about. Because I'm trying to run the enterprise operations not necessarily trying to help the data scientists figure out what the problem they're trying to solve is. I was just trying to get the data to them kind of thing. So, yeah. Yeah, the engineering is really result focused area, meaning you're the data plumber. You don't care how you will fix how you will you will fix everything but at the end you have to stop the leak or you have to bring this water from the water heater down to then to the faucet. I don't know how you do it but you have you have to do it so that's why it's. I have no opinion but it's a little bit more of a jack of all trade skill set because you have to know some sequel. Okay, if you're if you have if you have a background as a dba that's perfect you know this but you also have to know some about storage, because at the end of the day you have to start a data and the format the performance and all the different techniques around that are really important. Some networking will help if you understand the protocols, the efficiency of those protocols and everything it will be easier to move your data around so it's a little bit of all the things that are related to data that that that in my opinion makes if not a good at least a somewhat efficient that that engineer and that's what I see in my practice. You know we have started this project that for that which is called the data engineering jumpstart library, which I talked about this last time with Chris bloom and your Chris, it's about bringing directly usable patterns, what we call patterns, so that you can mix and match them together to create your data pipeline, which is about data engineering so those patterns can be simple. Okay, Kafka to history. I have some data flowing into Kafka how do I persist them in pocket format into object storage so that my data scientists can use it. Okay, this is a pattern that in the library, we describe we are publishing and sharing the code on how to implement that on OpenShift. That's one pattern and we are devising many more patterns in today so one is this one and maybe I could quickly show you this. Just share the screen. I will make. So you see an example of a pattern is trigger a Kafka event when an object is stored into my object storage. That's to create some even driven architecture whenever something new in store is stored in in my bucket, I will send information to Kafka. Another one would be, okay, whenever something is happening on my Kafka bus, how do I trigger a server less function a candidate function into my OpenShift cluster that will that will be able to process this event. So that's another pattern, and so on we we are creating all the things in the in the data in the jumpstart library that engineering jumpstart library, but that's the idea of mixing all the things together to be able to at the end create the pipeline you need to ingest transform store and make your data available. So whatever is needed in terms of skill set to achieve this will will define you as a data engineer. So that's why it's not a pure background in this or this specific thing. I've seen more and more people coming from various horizons, each with their own strength. But there are I guess there are some some things that you cannot skip. And the main one being SQL. You have to know your basics in SQL. At the end of the day, even if it's not SQL language, at least relational databases and those types of query. They, they, they, you will have to use them at some point in some form of another or another, or another, definitely. And the dog agrees. Perfect. chaos here this morning. containers building containers for data science we were talking about that a little bit in the pre show right like data scientists lean on the bleeding edge of stuff.  I'm focused on stability and resilience. So I'm going to run things like rail and open shift and you know, it's not necessarily the latest and greatest things but how can data scientists consume those things in an enterprise environment is kind of my question. Or engineers for that matter because I'm sure you all use some fancy stuff to. This is the, this is the other part. Okay, you wanted to go in. Yeah, please. Go ahead and stab at a call. Yeah, I was, I was just going to say, you know, what Gio just described, right, it's highly technical, it's unique to skills, it's, these are trivial problems right like I mean, these are hard technical problems that data engineers face all the time. Same is true for data scientists but they're hard and difficult problems are slightly different right it's, you know, we're talking about Bayesian statistics we're talking about loss functions we're talking about, you know, tons of math. But red hat brings to these fields is that stability and that consistency and there's a lot of repeatable patterns that we can do as data engineers and things that data scientists may want to start adopting from a lot of the teachings of DevOps engineers that will give us, you know, more organization or logical patterns and that allow us to go from one model to 1050 20 I mean like, you know, 100 that's that's the goal is the ability to cleanly scale because if we have complex environments if we have complex pipelines if we have data scientists writing their own data engineering code that differs from the data scientist, a few, you know, cubicles away, or, you know, streets away in their, you know, guest bedroom whatever. You can't just add people and expect, you know, linear scale right right I mean at some point this will taper off unless we bring those consistency, the consistency in the patterns that you know we are so good at red hat. And that's one of the first value proposition for open data hub or roads red hat open shift data science it's bringing this reproducibility because you know that was the case before it's still the case in many organizations that a scientist is okay here is your PC your laptop whatever and do some data science. Okay, first thing I will install Python and we install a bunch of libraries and call will do the same on his own laptop and Sophie will do the same on her own laptop. And then the end of the day, hey, hey, Karl Sophie can you look at my model. Oh, okay, they try to to load it. Oh no doesn't work because I have a library version one or two that one, and you build it with one the two that two, which I don't have yet doesn't work. And the end of the story. So, and again, that's generally how it works is this artisanal mode of doing things, everyone with a skill set and his right set of tools that fine tuned for their own way of working, which is not compatible to the industrial mode, the enterprise mode the scale mode that we want to do so the value proposition on the red open shift data science, open data base to bring this reproducibility because each and every data scientist will just spawn its environment, and it comes with the exact same installation of Jupiter of curated and selected libraries, and, and all the other tools that come with it. That's one way to to to ensure this for good disability, because then you have and if you do things properly as Sophie described, being, okay, this is the training of this model with this version of the data at this point, and there are tools to help trace those kind of aspects, then you are able to reproduce it on the other environment, which then simplifies the job for the software engineers, because they can directly say okay this model was trained with these sets of libraries so when I'm doing my package thing with this, and I know exactly which image I have to use which libraries I have to use, and then I just fetch the model that has been used with these parameters, and this I can automate, because I have the consistency, I have all the data. Sometimes we ask, okay, but I could install again a Jupiter notebooks by myself, yeah, go ahead, but that's not the problem. The problem is that you install it exactly in the same way for each of your data scientists, and that you do it fast. You cannot rely on Bob installing the laptops of everyone and doing the same thing exactly the same time. Yeah, you know, quick backstory from my old times. At some point as a project manager, I ordered a cluster of three different, three different, well, three machines cluster of three machines. Each one came configured differently. They were supposed to work in a cluster, but oh yeah because this I did the week after I did the first one the week before and then I changed this parameter and because I'm doing that the technician told me. Oh yeah, oh yeah, forgot to install this package guy, it's supposed to be a cluster. So you see, that's how it works in any organization. She happens. So you have to shield yourself for this so you have to automate everything. And that's part of what we do with Red Hat Open Sheet Data Science. We have automated the deployment, the library's curation and everything so that you have this reproducible environment that will be exactly the same now in six months from now and whatever. And you are then able as a data scientist to work on data science and not to figure out why the specific package doesn't work anymore with this environment because blah blah blah. That's the value proposition, much more than the tools themselves that are open source anyway. Yeah, all the tools are open source. Right, and I think, you know, on top of that we've talked so much about the communication that has to happen between data engineers and application developers and those data scientists. And it just makes sense to have everybody working on the same platform, you know, if everyone's on OpenShift it makes those communications, those sharing of work, and that, you know, reproducibility, not just between all the data scientists, but between the data scientists and app dev handoff, the data engineer and data scientist handoff, maybe handoff is the wrong term because it's, you know, such a back and forth, but it's early here.  So it, it's always going to be a conversation it's just whether you programmatically have that conversation, or whether you're just setting that thing up and you need to kind of whiteboard out exactly what you need to do. It's always always a discussion. But I have a funny little aside about me creating a model and passing it off to another data scientist for some, you know, QA and you know that was our process right we would check each other's work. For the longest time I wanted, I couldn't understand why my models would not build on her on her laptop, right and it would. It evaded me for an embarrassingly long time but you know, in that case we were getting different answers on these models, right. It wouldn't converge for her respectively we're getting wildly different results, but the, the, it's not truly failing. Right and it all comes down to their linear algebra libraries installed that most people don't know where they live on your laptop right and oh yeah I was truncating, you know floating points and she wasn't you know like neither of us knew that right so you know, there are times where you fail and dramatic red text and you know stack traces that's a good thing right if you're not running containers and you don't have that consistency you want the airs you don't want to be stuck in this endless loop of, you know, failing silently and confusion. I could see the. Yeah, if you're not standardizing right like if you're not creating a consistent environment for people to do whatever it is they're doing with the data, then you are going to have wildly different results, which leads to a lot of confusion which then goes into that business of what am I investing in, you know, and like creating a consistent environment to run models against to where it's flexible enough to say like, Okay, today we're just going to upgrade this version of, you know, by torch and this version of future hub, and we run everything and you know consume lots of CPU in the process and churn through all the data, but you get better results potentially right so everybody benefits from that at that point if you can redistribute that model. And when I say model here I mean the model of containerizing and the actual applications in use, not the data model sorry Sophie. That's exactly why containers are so fantastic for this kind of things because that's one way to achieve this reproducibility, especially and it's especially why it's true for many applications but in data science it's especially true because of the mathematical aspects of those algorithms which always have some random aspects and seeding aspects and let's say you have the exact same data exact same everything but you change the version of TensorFlow you are using but you know one minor revision the result will change. And even if you run the same data with the exact same thing exact same version of library, the results will change because of these fuzzy randomness aspects of the algorithms. So, as interestingly, the things are already skewed a little bit you don't want to pile on it and adding some more, some more diverging aspects to what you are doing. So you have to stick with okay I build this this is the container version with everything I need and this is the truth. And then you keep it because that's another important aspect you have to keep it, because in two years from now you may want to run the exact same thing against the new set of data but it has to be the exact same thing. Otherwise it won't work. Awesome. Well, we are rolling up on top of the hour we got about three minutes left is there anything anybody wants to share as we wrap up here. Any little thing to help that data scientists out on a virtual Friday. Maybe a second cup of coffee. Yes, I would need another pot. Maybe what I could add is, because of what I explained at the beginning, don't focus too much on the name or the title or whatever. There's a huge divergence now in between what people are doing and the title they have and the title with which they were recruited. You have to focus on what you want to do what you want to achieve your skill set because it's short in six months from now it will be named something else, but that's why that's why it's important to understand all those aspects. What was what gathered the data, clean the data, make it available, feature store and anything like that and then work on the data and then package it whatever names you put on this that engineering or whatever that's not even relevant. It's the whole process. It's the team aspect as a team of that engineer scientist software engineers, you have to put something into into production at the end of the day. So it's, if you have these clear site and understanding of everything that that's for the best. That's the advice I would give. Don't focus too much on all other engineer I'm doing only this or this. Yeah, maybe. Maybe today you're only doing that. All right, awesome show everybody. Thank you all for joining today. Appreciate all the insights as always. Thanks for having us Chris. Hey, anytime coming up next on the channel. I'm going to sit down with the one and only john Chapman junior for an episode of in the clouds. John is an advisor to our build the group of blacks united in leadership and diversity, as well as the head of red head it or director at it, which is by far the best it department I've ever worked with. Like hands down bar none right like they make life easy so it'll be interesting discussion to see like how they do that, and how they go about problem solving in that enterprise kind of scenario so please tune in for that at 11am Eastern 1500 utc. And until I see you again please stay safe out there and come back and watch us soon.


Well, when 16-year-old Brainerd High School student Jake Hapa-Jokey took his own life last February, it left the Brainerd community in shock. Now almost a year after Hapa-Jokey's death, his smile still lives on. Hundreds of people gathered at Brainerd High School yesterday to relaunch the Smiles for Jake movement. The goal of Smiles for Jake is to reduce the suicide rate through campaigns that uplift people's spirits and make them feel better about themselves. The first campaign was launched last night as people were encouraged to give high-fives to anyone they saw. What we believe is that you can change the course of someone's day. And you don't know, especially when it's a kid like Jake who smiled all the time. And nobody knew that this was how he was feeling. What if Jake would have had one more high-five that day or one more pat on the back or something for that day? You never know when you're going to change the course of somebody's life. Because of Jake's death, some students at Brainerd High School created the Lighthouse Project, which has already raised $75,000 to help students in the area of getting mental health screenings. Smiles for Jake and the Lighthouse Project will be working together in the future to fight against mental illnesses. Smiles for Jake is going to have a great hold on the community and we have a great hold on the students and obviously the students are part of the community. So we can just work together to get both the adults and the kids involved and just to get everyone in there. Mental health is a serious thing and to be able to talk about it really has to change. If you would like more information, the Smiles for Jake movement is launching their website on Thursday, February 21st. If you enjoyed this segment of Lakeland News, please consider making a tax- deductible contribution to Lakeland PBS.


Since the turn of the century, man and machine have met the challenge of the sky. Flying progressively faster, higher, and farther, circling the earth and spanning its oceans. This continuing progress in flight has been made possible by the responsibility, direction, and purpose of the dedicated men who fly, and to the efforts of the countless others who had to remain on the ground, but with their hearts in the sky. To become part of this modern jet air aid takes more than just the desire to fly. It takes education, training, and experience. It takes individual courage and the confidence that comes with being a member of a highly skilled professional air team. A team prepared to control the skies, capable and ready to meet any adversary on any terms, and win. This is the story of the aviator in this jet attack aircraft, and how he became a highly skilled Marine Corps jet pilot. Meet William T. Smith, Second Lieutenant, United States Marine Corps Reserve. He's just completed his initial Marine Flight Training and is a qualified jet pilot. He is reporting aboard the Marine Corps Air Station at Cherry Point, North Carolina to become the newest member of a modern Marine jet air complex. From the beginning, Marine officers set the example for others to follow. This is one of the reasons why he chose the Marines to share in this proud tradition. Lieutenant Smith chose Marine aviation because he wanted to fly with the best, perhaps in one of these giant transports, the largest aircraft in the Marine Corps, an aerial refueling station and troop carrier, or this mighty crusader, a supersonic, high-performance fighter, perhaps to fly this Phantom II, a high-altitude, ultra-modern interceptor, a missile with wings, or the sleek Skyhawk, smaller in size, but packing a powerful punch for the air support role. Lieutenant Smith will soon become an important part of the Marine Corps Air Ground Team. The first step in becoming a part of this flying team is to report it, turn it, his pay and health records. Bachelor officer quarters will be his permanent residence for the next few months. Neat, comfortable, almost like home. In the officer's dining room, old friendships are renewed, and new friendships are quickly established. Naturally, the conversation immediately turns to flying, careers, and future plans. Newly assigned aviators are greeted by the squadron CO, a combat-seasoned veteran aviator who will follow every phase of their training and progress. The orientation begins with an explanation of the squadron mission. To hold that fine edge of combat readiness will require alertness and complete dedication to the job. Fellow pilots are on hand to welcome and to meet the new arrivals who are invited to attend their first regularly scheduled pilot's briefing. Beginning with the operations officer, each squadron staff officer will instruct the new pilots in air and ground training and squadron procedures. The aviation safety officer is responsible for the flying safety of each aviator and for briefing on emergency procedures. The intelligence officer brings the squadron staff and all pilots up to date on the current world situation. The aircraft maintenance officer is an expert in the repair, maintenance, and modification of all planes. His job? Keep them flying. The flight line officer sees to it that all aircraft on the line are ready for launching at all times. The landing signal officer is always on hand to supervise field carrier landing practice and during actual carrier qualification landings. Before his first hop in a marine skyhawk, the new aviator must become familiar with every phase of local air control systems, a radar system upon which he will depend to guide him through many future flight missions. Since his main job will be close air support of marine ground troops, he must learn the characteristics of all ordnance. He rechecks the flight equipment he will use when flying the skyhawk. Each pilot's safety and maybe his life could depend upon any single item of survival equipment. Lieutenant Smith has ridden an ejection seat before, but now he must learn emergency ejection and bailout procedures specifically for the skyhawk. In the low pressure chamber, aviators experience the results of reduced atmospheric pressure at 40,000 feet. Special training devices demonstrate the actual, hydraulic, fuel, and lubrication systems of the skyhawk. They show what the plane will do and how it does it. Then there's training in the surface control system. With these controls, he can simulate all normal flight conditions. A pilot can actually see what parts of his aircraft are responding and how. This modern electronic guidance system trainer will help Lieutenant Smith become proficient in guiding the bullpup missile. Pilots practice maintaining positive control of the missile right up to the moment it hits the target. Upon completion of his ground training, written examinations are given, then reviewed and carefully checked by an experienced aviator. Errors, however slight, are immediately pointed out and thoroughly explained. Lieutenant Smith is now ready to take over the controls of a skyhawk in flight. He is also ready to discuss the advantages of a regular commission in the Marine Corps. The folks back home will be anxious and happy to hear about his progress, and especially his decision to consider a regular commission. This will be good news to someone else, a special person who is not part of the family. Not yet. Thumbs Up signals the start of his first in a series of familiarization flights, with an experienced chase pilot flying alongside. This is an exciting moment for Lieutenant Smith, now in command of a fast, sleek Marine skyhawk. The enormity of pride in personal achievement is only surpassed by the faultless precision and coordination of this pilot. First of many night hops in the skyhawk is given at dusk. You provide the gradual transition to complete darkness. Lieutenant Smith's confidence in his instruments and navigational aids reflect the results of training and practice. A vital part of Lieutenant Smith's training is the preparation for the air support role. Close air support is a powerful, accurate, devastating weapons system. This can deliver tons of bombs at low altitude and with pinpoint accuracy. Lieutenant Smith zeroes in on a ground target. Twenty millimeter cannons speaking with authority. Lethal ballistic rockets delivered right on the front porch of the troops on the ground with the ability to knock out tanks, artillery, or heavy fortifications. Actual training like this demonstrates the effectiveness of close air support and coordination, both on the ground and in the air. With complete confidence that when needed, they can make each shot count. Two-air refueling requires precision flying and can extend the effective range of the skyhawk to distances practically unlimited. On long distance hops and cross-country flights, Lieutenant Smith puts into practice his navigational training. He has computed his own flight plan, then has maintained it by varying his airspeed, altitude, and headings, the city below. A familiar sight will be its first stopover. Lieutenant Smith never thought he would be landing a marine skyhawk in his own hometown. He is always exciting to return home. To be in familiar surroundings. A welcome break offering an opportunity for an evening out and a chance to discuss future plans. Lieutenant Smith puts wheels down for his first simulated aircraft carrier landing. It is high-performance skyhawk. Using the mirror landing system, the orange meatball must remain in the center of the mirror or the landing signal officer will signal a wave-on. Even under ideal conditions, carrier landings can be demanding and every approach must be exact. Success comes with practice and confidence comes from perfection. Afterwards on the deck a critique is held. The LSO and the pilot will review every approach in detail when and why each correction was necessary. Lieutenant Smith is learning that perfection takes work. Carrier operations are the culmination of all the previous grueling hours of practice and study has paid off. Lieutenant Smith makes the approach for his first actual carrier landing. With a firm hand and steady nerves, he lands like a professional. Marine aviators taxi their jets into position for a catapult takeoff. They are ready. And wait for the command that will send them screaming skyward. The flying leathernecks leave the carrier at sea. Headed ashore to soften up the beaches for an amphibious training exercise. Marines in the air. Supporting Marines on the ground is an unbeatable team. Geared for modern warfare, divers can now direct the skyhawks to inland targets. Another successful air support mission completed by professional aviators. Doing the job they were trained for. Like Lieutenant Smith, college students across the nation are choosing Marine aviation for a career with an exciting future. Dedicated young men who stand ready to be launched from bases and carriers around the world. Accepting the challenge of the sky. Fulfilling their desire to fly. With the knowledge that they are taking an active part in our nation's defense and future.


This is the definitive guide to getting in the zone. I'm Brett Wingyer. I'm a neuroscientist, biomedical engineer, and athlete. First of all, let's talk about being in the zone. What some people call a flow state. Basically, you're in the state of perfect focus where you're not even consciously thinking about things or processing moment by moment. It's a state of relaxed, high performance. That's the zone. The fact is, anybody can be in the zone. It's not just sports. It's anything where you're able to just do it and block out distractions. Repetition, repetition is the language of the brain and it's the way you get better at anything. Here's one study. The National Institute of Health put freestyle rappers in an MRI machine while they were freestyling. There was less cognitive control because it didn't need to happen. They were in flow. They were in the zone. They were practicing their craft. When you're first learning something, everything lights up here in the frontal lobes. As you become an expert, then that activity actually goes down. All of that gets moved into the subconscious. So the biggest misconception about getting in the zone is that it's this magic thing that only happens couple times in a lifetime. That's not the case. You can prepare for it. Be well rested, smart, hard practice. Ignore distractions. Part of that could be meditation. Interestingly, caffeine actually does improve performance. You have to find that happy medium. A little's good, a lot's too much. True or false? Sports performance is all about genetics and how big your muscles are. Absolutely false.  All those movements that make you an elite basketball player. It's all part of muscle memory. One of the best is this guy, TJ Carey. He's a cornerback for the Oakland Raiders. We work with TJ with our product, Halo Sport. It's a pair of headphones. It helps speed up firing neurons in the primary motor cortex, which means the brain can take in more information really quickly. And he added nine inches to his vertical leap. 42 inch vertical leap. That's amazing. That's insane. His brain was optimizing the firing of every little part of that muscle and everything that goes into that. Strength is specific. Let's say you're training on a biceps machine. Then what you're really getting best at is just doing that exact thing, is doing that biceps curl. Because it's not just about how big your muscle is. It's about how your brain has learned to use it in exactly that context. And that's why the best physical training is the training that best approximates exactly what you're doing on game day. The thing is, this is relevant to all of us because we all have a brain. We all have a body. We all want to reach our potential. Everybody can have this sensation of, wow, I nailed that.


Hello, my name is Ursula Pike and I am the author of Origin Stories, Pathways to Open Education.  Before I tell you about the OER I created, I first want to tell you a little bit about me so you'll know why I created it. I work for the Digital Higher Education Consortium of Texas. I'm also an author and a creative writing instructor. I'm involved with the CCC OER's Equity, Diversity and Inclusion Committee and I'm a member of the Karuk tribe. Our homeland is on the Pacific coast near the California-Oregon border. So all these parts of my identity contributed to me wanting to create an OER that promoted equity, diversity and inclusion but I didn't know how best to do that. At Open Ed 19, before the pandemic and before the nationwide reckoning with race that happened in the summer of 2020, I saw a poster presentation by Melina Thied from SUNY Plattsburg with this data right here on the lack of diversity in OER creation. This data is upsetting, not surprising and sadly actually a little bit better than traditional textbook publishing which has even less diversity. To me this was one of the most important presentations at Open Ed 19 but it wasn't until a year later when I began my Spark Open Education Leadership Program that I was able to do something to address it. I ask the participants of Origin Stories six questions. Tell me about yourself and how you came to Open Education. How do you see your unique identities intersecting with Open, if at all? Tell me about an Open Education project you've been involved with. I asked them how did they see their role in the future of Open Education. I asked them what the biggest benefit of Open Education was and what's missing and then I asked them what questions they were grappling with related to Open Education. In response to the first question about how they were involved with OER in the first place, Tanja Connerly said my department chair came to me and asked. Shinta Hernandez said I became involved in the grant as a sociology professor and Angela Debarger said it started to click for me the connection between what we wanted to accomplish and the intention of OER. How would you answer this question? How did you become involved with the Open Education movement? Were you invited in? Were you just curious? Were you handed the responsibility for OER? Was it something that you thought would be good for your career? There's no wrong answers here but I think it's important to look at at the answers to determine who are the gatekeepers? Who are the bouncers? Who are the doorman of Open Education? Who let you in and who are you helping if by nothing more than your example? How would you answer this question? How do you see your unique identities intersecting with open? If at all, what are your identities? Are you a researcher? Cuban American? Instructor? I ask the question because each of us contains a multitude of identities. I'm native but as I said in my introduction I am also a creative writing instructor and sometimes my desire to find openly licensed materials conflicts with my desire to provide my students with writing by diverse authors. I'll always choose diversity over open resources.  The next question I ask was what do you think is the biggest benefit of open education and what do you think is missing? Jesse Lawyer said for Indigenous people open access especially related to our collections can help us reunite with those relatives that are held in collections. Shinta Hernandez said we can still improve we can develop more accurate assessments and Angela de Barger said open education can play a role in system transformation. I want to highlight one more response to this question. Ariana Santiago said what's missing is balance. I think that can apply to our work in open education and I know definitely I feel that sometimes applies to our work in equity diversity and inclusion. How would you answer this question? What do you think is the biggest benefit of open education and what do you think is missing? We all know open education can save students money but what other goals do you or your institution have that are addressed by open education? The final question that I asked the women in origin stories is these are my questions but what questions about open education are you grappling with? That's the question that I'm asking you right now. What's the question about open that no one has asked and that no one has asked you? So what's the point of my project of my interviews of origin stories as a whole? I have been asked how an instructor or an open education practitioner can use these stories to address equity and the truth is it's not a straight line. You can't say Angela de Barger said this in origin stories so we should do x. In diversity equity and inclusion in open research and education Tara Roberts asks who is missing? Who isn't even in the room? Who doesn't have a seat at the table? Who is sitting on the margins? Who doesn't feel welcome? Who has to fight to have their viewpoints respected? When I asked these questions about the open movement it was clear to me that as a woman of color I felt like I was sitting on the margins so I created a project that centered women of color. I hope that the efforts of these leaders and many more who are working to transform education using open are recognized. I hope that this presentation has made you a little bit curious about origin stories. You can access the google doc of origin stories at my page on the spark open education leadership program. Watch your favorite open education listserv because I'll be publishing the full resource via press books soon. I also hope you're inspired to tell your own origin story. Thank you and please follow me or reach out on social media if you have questions.


I am in a unique position to see what Hispanic ministry was like 15, 18 years ago and what it is today. Before the pervasive reality was that Hispanic ministry was being done on the side, right? And Hispanic pastors were, you know, the associate pastors of the Hispanic ministry at a church. Now it's different. Now we're seeing, you know, pastors that came from Mexico or other countries or race, born and raised here that are leading bilingual ministry, not just Hispanic, but bilingual ministry and they are leading the church. And they are not seeing through the lens of, you know, oh, you are Hispanic, no, you are the pastor of the church. And I think that is a huge improvement or blessing because before I believe there was a different mentality, both in the conference and in the local church, and now it has become healthier. And I think that is exactly what we need to experience and encourage, you know, among us. That's how you grow a forest, so to speak. You plant the seeds, you water them and over time you begin to see the results. So by no means we are done. This is just an encouragement to continue the work that we are doing.


Thank you for watching this video. I hope you enjoyed it. If you did, don't forget to like and subscribe. See you next time.


We spent a week with two tank crews from Charlie 164 armor, so I think they've done an amazing job I am proud of those guys very proud Just getting ready preparing for the shot to get a shot to go to the southern cup I have learned probably more in these last few weeks than in the other training exercises. I've done combined We met team cannon Arke and team Count Dracula, and they went head-to-head for six weeks I'm getting the chance to represent an entire division I'm really grateful for the opportunity It's really humbling Competition brings out excellence. That's driving us. We're driving them and it's all in the spirit of competition to make a better team These guys want to be the best. I'm ready. I've been waiting for this moment But in the end, one team gets to go to the Sullivan Cup They've wrapped their head around this. We're getting an opportunity here to show that we are the best in the world You get to play on the big boy platform This is the Super Bowl of Tanks They want this This isn't your mama's table six qualification And this is a this is a test to get after the best tank crew in the United States Army We're gonna push everybody to limits We're gonna make it physically demanding We're gonna test your your cognitive skills when you get out on the sticks exercises to make sure you know How to do the basic functions it takes to be a tanker and then we're gonna make sure you can come out of here and hit Which is shoot at Certain Fauntler Roy's crew was picked to represent the third ID The way the Sullivan Cup works is you show up the issue you a tank and you use it to compete in a series of events for points So the only points you care about are to get into the top four after that Nobody cares about your points because the shootoff is the only thing that matters the shootoff determines who is the Sullivan Cup? So take us through the competition briefly the the different stations what they do and how to get to that last phase Well first they have competition which they called day zero was Sunday that was the armor cleaning proficiency test It starts off with ammunition lift That to get a hundred points need to do it 45 times. I only can do it 36 Trackbox shuffles 20 meters you take 10 track box and move them back and forth Then you have a cable drag for 15 meters You pick you drag the cable pick it up and you just run back Then you have a road wheel roll for 240 meters basically a baseball diamond and you have you finish off with a one-mile run And they posted the second best crew score. So we finished in second place The mounted sticks lane comprised of a land navigation course and there were five stations within the land navigation course The time begins now The first station was their start point and that was prepared for combat They prepped their vehicle Got ready and then they moved out and OC followed them And then things got interesting their tank broke down Basically started our movement once we got up here to this intersection We was doing little tactical policies to make sure our bearings were good and a Lot of times with sometimes with certain problems that the tank may run into it'll self-aboard itself pretty much like a I guess Self-preservation if you will Essentially it was the tanks check engine light coming on. Yeah, this is where the fault showed up again. It's not showing up So they did some troubleshooting I think we good And eventually they had to pull over and just wait for another tank These aren't their tanks that literally jumped on this tank a couple of hours ago So tanks break down. That is what it is They eventually to transfer all their gear and they got back to work They dealt with the little three-hour hiccup and then they moved on they got to their next station Which was maintenance They had to break and replace a two-block section of track They got beat on time on that one. They still got 60 points because they're doing pretty well They found all the rest of their land nav points and it it got really late It was about 20 hundred 20 30 by the time they got to that the medical sticks And he's showing no signs of life and they executed got 88 points on that did really well We went uh We went up to station five which is again at the containment area And they did vehicle id and that was pretty rough But they kicked the four station right in the seat It was a occupy a bp did really well and they got 100 points on their land nav as well So the end of the day they had 811 out of a thousand points and going into the gunnery They were in 10th place out of 16 crews The digital multi-purpose range complex at fort benning is impressive and it'll test any tanker This is not like any other range that i've ever been on. It is not a bowling alley. It is not flat terrain And so it's forcing these crews and it forces our future leaders to completely expand everything that that they know about tank gunnery Gunnery is one of the crew's strengths and this is where all that degraded training at fort steward Hopefully would pay off Right now and they'll do the pc tank rpg team degraded mode and then they'll do their call for fire Who you'll have coming up right now as you see approaching the bp is 164 armor So if we have any 164 armor fans right here, there you go your chance to cheer for I overheard you um talking about 164 what was it you said about that They had they were having mechanical issues. They took them back to the room. Nothing they could fix out there So our crews out there, um, they got one round off and now they've had a malfunction What we don't know is is uh, is if they're going to be given an alibi Which means if it was a fault that was uh, something they couldn't prevent They'll give them an alibi and this won't count against them if it's something that they they caused It it counts against them. So not a great start to the gunnery But it's it's what happens It's it's part of being a tanker and I think that you know what they learn through all the training Is that you've got to be able to adapt this so we're waiting to find out what's going to happen What if I came out I'd swear this was yet another setback But our guys put their time to good use to make sure they were ready if they get another shot Yeah, that mound is gone Try it again So we got good news today our team found out they got an alibi And that means that when the round got stuck in the breach It's determined that it wasn't the crew's fault So that mean they got to start fresh and they went out and they shot really well Took second place on all the teams and that's kind of like breathe the new life into them So we're here now at 10 o'clock and our crew's actually out on the range doing a night fire We also heard you guys took second in the day run Second highest score in the day run. Yeah You did a good run on the night night Uh, it could have been better if I just Trusted him my tc more. I couldn't identified a truck and troop target But he could so It is what it is. I mean I think corona has beaten himself up a little bit. He um They had a their last engagement was uh, they had they fire off some flares to simulate that they're being fired on And and he he didn't see him They're in it to win it and they're they're working through the The challenges, you know tomorrow's the last day to give it their all and See where the cards lay after that Think think about the crew that wins this thing when then when they go home and they turn around and say, you know what? We won this and this is how we did it no way No, no, no, they're in third place, man Dad they came up from third from tenth to third but they the They got second best last night wow Back on the map Urgent good news, man. Yes. We're here. Yeah So today you just got to go do what you do three got three engagements Three engagements to solidify our position in the top four and uh It's game one. I know you're excited, but but you know having followed you guys here I'm going into the final gunnery. They were confident. They were focused You know barring any major malfunction. They should make the top four Looking at that. I don't have any affirmative things to say that why you miss those targets If you're good at reading facial expressions, their faces speak volumes Four seconds when you moved up to you engage getting a doubtful left Phrases like doubtful left and over or short are not what a tanker wants to hear in an aar They got it over on target three. It means they missed And that's out of a possible 300 all right, I'm going to put you guys on a little break Sergeant first-class matt nigran delivered the news with as much encouragement as he could The wait now was to see if they'd go home Sooner mess no news is good news right now We're waiting on two more crews to finish their aars and the final scores to come in To notify you on the points. That's how tight this competition is getting Where's the field looking like? I can't say I just can tell you no news right now is good news because that means We're not sending you. We're not sending out the door right now all right, um We are back at the hotel and we are waiting for news. Uh today was a crazy day. It was a roller coaster ride When we arrived this morning Our guys that we knew were in 10th place And it's actually on the way out that we were checking the the facebook site and found out that because of their night gunnery They had moved to third place So the last phase was today and that was their last day gunnery And um, they did not do well. So You know, we're just we're waiting to hear You know whether they make the top four because the top four is where they need to be To be able to compete in the gunnery tomorrow and uh, and if we don't hear tonight, then you know, we'll just find out on wait a minute They said they might text us tonight. So Give me a sec This could be it I think everybody who is still in the army has an obligation to make sure that we continue to be adaptive Uh and innovative and continue to To strive for excellence So if we can instill that in our junior ranks and the soldiers that are coming through this competition Think about that 16 crews that now can go home to 16 different places and talk about what it takes to be the best here So there was a tie for fourth place, which means five crews went to the shoot-off and our guys They missed it by one shot I have a better picture of exactly what we need to do to truly become More proficient on the crewman We're a bad tanker. I learned I learned that we're better than we thought we were Really this competition is about seeding excellence or planting the seeds of excellence in the tank community A great honor to come down here and compete for the best and I mean I'll be back in two years. I'm gonna win in two years. That's that's what i'm taking out of his motivation to come back It's a great experience. I mean in my eyes. It's a once in a lifetime thing I mean it's not every day that you're gonna go somewhere and have a best tank competition And your name just so happened to come pop up Any team that goes is exposed to the training and all these other great tank crews in the competition That make them better tankers as a whole Yeah, you have a winner, but everybody that comes is better for it. It helped our resilience a lot overall They helped my resilience a lot. I'm I'm I'm a pretty resilient person before this but this made it this made me even stronger with this competition I was honored to be in a position to give the best of the best to run for the money I was honored to be in a position where the best of the best were like, oh man We got to watch out for those guys from third id Can you tell stories like this? You always hope your team's gonna win and our guys didn't But I wouldn't have it any other way because what we saw is the level of resiliency and that these guys No matter what they kept running hard after their goal. I mean, that's what it's about. It's about not quitting It's about perseverance and they showed us the best of that


Good Omen's season two. How can there be a season two when season one was about the book and it covered the whole book and there is no second book? What is season two? We were all wondering does this mean that for season two they're just gonna make it up? But then Neil Gaiman, wiser than all of us mere mortals, did point out that the original was also made up so. So Good Omen's The Book was written by Neil Gaiman and Terry Pratchett. It is a standalone novel and it is about an angel and a demon working together to thwart the apocalypse. As I said, season one of the show covered the entirety of book one. Valentine's and Purposes would be expected to be just a mini series, not an ongoing TV show. But it was really really popular, really really successful. People really really liked the main characters so obviously when there is popularity there is money on the table so people are interested they will make it. But was there anything left to tell? So this book is about the apocalypse and spoilers they prevent the apocalypse. So like where do you go from there? Is there is there anything left hanging? Like is there any further story to tell here? Is like a natural story thing to happen that you could be like oh well season two could obviously be about you know that? Not really? But also yes there definitely was. What comes after the apocalypse? A lot of TV shows have this problem. So in the first season they'll have a pretty low stakes but menacing villain and then in the second season they have a more menacing villain with higher stakes and then season three they have even more villains with even higher stakes and then by season four things will start to go off the rails and then season five will just be chaos. Good Omens the book was a standalone novel that was not trying to plan for a multiple season story arc for a TV show. It was just a book that two dudes wrote together. That's it. So they weren't really like trying to pace themselves. They went straight for the apocalypse. Where do you go from there? Do you try to escalate even beyond that? Like something bigger and more impressive, more alarming, more bigger in scope than the apocalypse? I mean I guess you could do that but if you're not going to do that then what are you going to do? So here I want to briefly talk about Game of Thrones. So season eight of Game of Thrones was a disaster. I'm not going to go into any specific so if you haven't seen Game of Thrones or you haven't read those books you don't want to spoil. I was speaking to you but born obviously because everyone else has seen it already. Season eight was by all accounts terrible. I don't know of anybody that thought it was good. That includes the cast and crew. There were there were many reasons that season eight was bad. There isn't just one reason for that but one of the biggest reasons and surely the reason that like was most important probably to me and to people like me, readers like me, is the characters. There was a lot of like world building shenanigans and things like that that did not make any sense, did piss me off were bad but the the most egregious thing I think was the characters. So the characters in the show even while they were still on book there were changes being made and the characters in the show even to begin with were slightly different from their book counterparts and as the show went on and as more and more small changes and tweaks were made to their storylines and to their relationships and then when they went off book then their storylines entirely kind of like shifted and changed based on what the show runners were had in mind. By the time we got to season eight the characters that were in the show were no longer the characters that are in the books and the endings in the show if they are truly endings that were gotten from George RR Martin if that is in fact the case which I don't know for a hundred percent certain that all of them are. Assuming that they are that these are the endings that the writers had heard from George RR Martin that this is how it ends up this is how the story is supposed to end this is where those characters end up this is who does the thing this is who ends up with who this is who dies this is why they die this this is the end point that is in is going to be in the book someday. So assuming that is true that still doesn't work because the characters in the show are not the characters in the books anymore. Having this this the plot of the show again assuming these end points are accurate end points have taken these characters that have kind of like started to off-road from who they are in the books like forcing them to fit into endings that no longer fit those characters that no longer make sense for them just to force them into fitting the endings that are in planned for characters that they aren't them anymore made it feel I mean again there were many problems like pacing and writing issues as well but it wasn't true to the characters anymore like these endings for them or at least not the way we got to them so season eight would have been much better off if they had written different endings if they had written endings that fit the characters that or in the show now which are different characters instead of forcing them into these kind of predetermined end points that have nothing to do with who they are anymore okay so back to good omens why would people want a second season of good omens other than just kind of like generally wanting it so in the book Crowley and Aziraphile the angel and the demon they are friends they are very good friends they work together despite being one a demon and one an angel you know they find common enough ground to work together and to thwart the apocalypse like that's a pretty big deal so so they are quite close friends in the show Crowley and Aziraphile are close friends but they are possibly more than that so good omens of the book it could have a sequel like it's conceivable and I believe Terry Prouchett and Neil Gaiman had spoken about possibly writing a sequel someday they obviously never did but that that that was an idea certainly that they had that they might write a sequel to it the sequel that they've written in the show would not make sense as a sequel to the book when the show originally adapted good omens just the original book into the original first season of the show it was a quite loyal and faithful adaptation but nonetheless there are tweaks made there are changes made there is nuance and color added to things just by virtue of one it's written for a modern audience so certain things are just kind of like updated to kind of suit the technology the sensibilities the humor just everything's for it so it suits a modern audience better but also just like with Game of Thrones the actors playing these characters bring dimension to these characters that goes beyond what is on the page it goes beyond what is even in the script part of the reason that Crowley and Aziraphile have this kind of extra additional something in between them or at least the audience perceives them too a lot of that is to do with simply Michael Sheen and David Tennant how they played the characters and the amount of chemistry that those actors have together playing those characters a show is a visual medium so there's a lot a lot of it is subtext a lot of it is just again simply the way they're playing these characters and it's not necessarily what they say it's not necessarily what they do good omen season one is a quite loyal adaptation of the original book but again the way that they look at each other the way that they stand near each other and the way that the show runners would then even lean into that in the way that they would frames the scenes so the music cues that they would use these are all like subtle things that aren't in the text it is subtext it is it is just like adding coloring and adding flavor to how you are perceiving what is going on it creates this added dimension to the relationship that is really only present in the show so the screenwriters for season two of good omens did not try to write a sequel to the book good omens they wrote a sequel to season one of good omens the show they wrote a sequel for the characters that as they are in the show so um i quickly want to talk about doctor who i want to say this is the last aside but it's not um neil game it has written for doctor who and there are certainly elements of season two and i guess even of season one just sort of of gaming style that are doctor who ask and doctor who frequently has this problem although i would argue it's not really a problem for doctor who but it has that problem that i identified earlier of if you have the apocalypse where do you go from there and the thing about doctor who to be clear i have not kept up with doctor who i i watched like through matt smith and in the beginning of peter capaldi and kind of fell off after that so i'm not talking about like the current iteration of doctor who i have no idea if it's any good i've heard pretty negative things but i don't personally know and don't have any personal opinions about it because i haven't seen it but i'm talking like christopher eccleston my favorite doctor david tenet obviously matt smith and then i did like peter capaldi but not enough to continue because i didn't think the writing was as good anymore anyway that's not what i'm that's not why i want to bring up doctor who doctor who in general from its genesis like the original doctor who all the way through to presumably the president again it the doctor faces like universe ending you know storylines frequently and the doctor faces mortality and there was supposed to be only a certain number of times the doctor could even regenerate because this even this immortal character had you know a shelf life and even then it's able to keep going after that the reason that's possible is because the reason people love doctor who is not because of the world ending universe ending stakes of the episode or the season or of the show people watch doctor who because they like to see the relationship between the doctor and his companion or companions that's the heart of the show and so we can have the apocalypse and then we can come back again for a story about shenanigans where we go meet shakespeare in history and just have a little adventure and that works fine and it doesn't feel like a disappointing deescalation because while the apocalypse was exciting and nail biting we were here to see the doctor and his companions and we're still here to see the doctor and his companions so back to good omens in the case of season two this i think is a good example of fan service when it works it's doing what it's supposed to do just like with doctor who the reason that people loved good omens or at least one of the reasons people loved good omens and one of the reasons people would want a season two of good omens is because they love seeing crowley and aziraphale together just like with doctor who it's not about the apocalypse it's about seeing these characters together it's about seeing the doctor and his companion but the writers didn't just give people what they want they could have just filled it with like lots of cute scenes between aziraphale and crowley and been like that's it that's all we gotta do show those two together and then everyone will be happy instead the writers neil gayman and whoever he worked with i know he didn't write it by himself understood the characters in the world that they had written and established in good omens and the characters that they had established with michael sheen's and david tenon's performances of those characters understanding who it is that they are working with and understanding what kind of a story we could build around that that would feel true to the world and true to the characters and so while we do get a lot of aziraphale and crowley they've also you know built a story that they're going to be dealing with this is another the doctor has a new adventure a new problem to be dealing with a new crisis that needs to be solved but because they know what type of story they're telling and they know what it is that people are here for they have written a story where the the crisis in question the universe thing going on is interesting in its own right but it also works as um a reflection of in concert with the emotional and personal story going on between crowley and aziraphale and so while the like big thing is happening the things about this big thing they are an opportunity to further explore the more intimate story of crowley and aziraphale because as i say it's a reflection of it's an expansion of it's um a mirroring of the the things that are going on internally with those characters and so they've cleverly had this kind of adventure going on which is the thing that they're occupied with handling be the mechanism through which they're able to explore the internal story that's going on between crowley and aziraphale who we are here to see so now i want to talk about marvel this is the last aside i promise so marvel has many problems obviously and um i'm not the hugest fan of marvel with marvel um people loved the big team up the big epic crossover movies where it's like the the big big big big bad the universe ender all the superheroes all at once like it's very exciting it's very epic we love to see it but this made marvel think that that's that's what people love so we have to keep doing that and the reason those crossover stories were so good is because we had to become more intimately familiar with these characters in smaller standalone stories and then even after that when we have still liked these stories it's often been because we've returned to that so much like doctor who where you can have the apocalypse then you still have to come back then and have some smaller stories that are just about the doctor and his companions after that because that's what makes people care so here in their marvel has done something right at least in my opinion so with things like wand division where we weren't doing the apocalypse anymore we weren't doing every single superhero of all time teaming up to defeat like the end of the universe wand division is a really really intimate story a really really small scale and small stakes for the most part people really really responded to it because it was really different in style and tone really different in in scale and scope and it was an intimate character study so we could really get to know this character that we had only really briefly seen in the big crossover epic movie so marvel doesn't seem to understand that the strength of its stories lies in its characters and it doesn't really allow them the room to shine and to grow so back to good almonds good almonds understood that you didn't have to now do keep doing the big things what we're here for is the characters so we can still have some fun times we can still have some adventures but it's actually totally fine to now scale things back and just tell an intimate character story or at least a more intimate character story we don't have to keep going bigger and harder and more and better and you don't have to do that we can bring it in again we did the big apocalypse we can bring it in again we can now talk a smaller story a smaller adventure smaller scale thing because the thing we are here for the thing that we loved about the original it was a zero fail and growling season two oh good omens was it any good i think yes it was never going to be as epic or as good as the original good omens that's not a reasonable standard to set either for the writers of the show or for a viewer watching it but it it didn't try to be it wasn't going to try to outdo the original good omens because that would be a recipe for failure so unlike marvel it did not try to go bigger and harder on stakes and epicness unlike game of thrones it didn't try to write a storyline around characters from the books which aren't the characters in the show anymore like doctor who it understood that you can have the apocalypse and then you could also have a tea time afterwards and that people will like both if they like the characters and i think a lot of other shows and movies writers and producers could take a lesson from good omens and adopt this approach when they are writing their series and sequels and multi verses etc etc etc let me know in the comments down below if you watch season two of good omens what you thought of it if you enjoyed it if you did not enjoy it if you completely disagree with everything that i've said or if you completely agree that's always nice to hear as well whatever you want to let me know i post videos on saturdays other random times well but on saturdays so like and subscribe to my patreon if you feel so inclined and i'll see you in the next video


I was going to a government school in Cameroon and life there was like way different than what I'm experiencing here now. It's always been my goal to make the positive change so my mom was like you know someday you're gonna be in the United States. I did not believe it but every day I slept on my bed I'm like okay this place where people are saying dreams come true you know with this milk and honey that's how we know America back home. I dreamt every day of looking at the blue sky in the summer and it just felt unreal but I just kept a faith. It gets hard for international students when they come here it gets hard for them to find food it just shakeens you and you need that support you need that person who can bring you to the center and be like okay don't worry everything will be okay there's just something about growth that really speaks to me personally like I think the more you grow and get to understand things the more you can understand your environment and be able to relate with others around you because my focus is all about people around me I mean people please.   My goal is to instill this confidence and tell this and be able to tell the students you can do anything. The teachers the professors the deans all of them are here for you so you can do anything you put your mind to.