Hey guys, welcome back to my YouTube channel. This is Daniel Rosal here. So I wanted to record a video today basically just explaining what I've been doing over the last few months trying to keep a track of the Technical aspects of my video production. So I started doing YouTube videos the best part of let's say a year ago actively starting out with some kind of screencasts and tutorials and showing different how to do different things on Linux and stuff like that and I always kind of plan to do something a little bit more than that and still very much my aspiration Feel like I'm just scratching the surface but as I scratch the surface one of the aspects of video production that I feel very much drawn to Perhaps just because I'm kind of of a technical bend is the Technical aspects the gear the equipment that go into making video whether we're talking about Video blog such as when you're working with the one you're watching or videos out in the fields Shot with a camcorder and all that now What I like to do is Keep track of what I'm trying out and as this has gone on this hobby has evolved I've sort of started to build up my first pieces of gear and Keeping track of what I'm using what I have Becomes not difficult but slightly more important particularly when you're talking with people on videography forums or camera forums or you want to send somebody a link and Get their feedback on what did you think of the audio on this clip? What did you think of the lighting? What am I doing wrong? What could I be doing better? So for that reason those reasons and many more I've developed a couple of Been doing a few things and I wanted to just Show these things in case you're starting a YouTube channel and of course, this is not prescriptive This is just what I'm doing and I'm offering it out there to the world in case You think it's a good idea and I might work well for you. So Let me Get into us. So firstly first thing I did was build out a spreadsheet or I built out a spreadsheet And that's basically keeping track of what I currently have in terms of video and audio equipment Now I'm gonna just move myself down a small bit here to show you What I am using or what I've done to make the spreadsheet. So I'm gonna actually put myself over here So you can see the tabs So this is a spreadsheet that it's a work in progress as I am acquiring new things. I'm adding them, but I've Divided it according to a category. So I have starting with my cameras, you know the Canon Vixia HF R800 the kuau 20 and P action cam believe me It gets hard to remember all these different product names as you build out a kiss lenses I'm actually about to do that do that tonight along with lens filters because I just picked these up In the last week from Amazon while in the US. So I need to just take notes of Wash manufacturer Man, man, you factor or product Lens Etc. And that's kind of how it works. And let me show you one that I've actually fleshed out a bit more than this So here's an example. This is the microphones one and this is where I'm keeping track of my Burgling microphone collection now. I think audio is something that I find really interesting I've sort of dabbled in podcasting and audio production before this YouTube journey really kicked off and Therefore, I've kind of already had a few microphones Definitely not very high-end microphones. I am doing all this on a budget And what I'm trying to do is really cast a wide net to have one of everything Even if those things are not super high quality. What I've done here is just kind of noted the information that I thought would be useful manufacture and model and then I have Cancadenated these two columns because when you're posting on Forums whether on reddit or the kanem community or whatever else you typically refer to a piece of kit by Manufacturer model and it just gets really hard to keep all these straight You've got the Beetran WXM04 and the Mammon KMD2 and the Maono AU902 So there is another purpose for this and that will become clear Just in a couple of moments. I'll show you what I'm adding into my youtube video descriptions So I've noted the manufacturer model. I've noted the microphone. I've noticed noted what type of microphone that is connection type Analog or digital batteries. I've even put down what type of batteries the thing has and if I don't have the info I just kind of google it and Try to source it. So this is a for example, this is a Mmm. This is the powered lav mic This is the powered lav mic. The question is does it list the battery probably does or is it? I think this is an updated model that's charged from a micro usb So anyway, that's what I do. I try to create a good information source bringing all together the information because I buy stuff from aliexpress I buy stuff from amazon and I wanted to have this central look up so that if For some weird reason I happen to be outside an amazing camera store That is holding a flash sale and I say oh my goodness. What do I need? um, I can easily look up and see what's in my current microphone box and What might be the next logical upgrade? If you have any feedback on these microphones besides the fact they all kind of suck Do let me know in the comments.  That's I I've developed a relationship with these microphones and some of them are they're all None of them are outstanding, but some of them get the job done Um, so lighting is the next one and I've just noted and so on and so forth I'm not going to go through every tap here Um, so that's my spreadsheet and as he said it's a work in progress The second thing I do is um, I'm just checking the time here six minutes I have been for a while adding in These descriptions called I call them production notes. I don't know if there's a better word Um, and basically for every video I upload to youtube What I've done here is in the upload defaults. There is this setting for a default description And so that I don't need to type it in every time. I've just copied and pasted it here And I'll show you what my default is. It's basically uh, the camera Used plus the audio source. That's the microphone plus the stabilization if any Plus the lighting equipment used post production I just typically write did I do it if I don't do it? I write nil if I do do it I write kaden live or if it's another pro eventually I hope to graduate to davinci and then if there are some other notes, um, I put them in there too And that's in my upload defaults now I have found that the upload defaults functionality is a small bit buggy Therefore I do find that it sometimes doesn't get included in things I upload from my phone Um on youtube and therefore what I do is I go in batches and I will update Previous clips. Uh, so I just actually did this one for a previous take that Did not go so well. Um, so I'll just do this again and pretend you didn't see this So I just take a note and this is why um So this is why this is very useful because um, I can't remember the the name of every single Uh microphone in my collection, even though there's only like 10 microphones in there But some of them are kind of new to me um, so between the maonos for instance, um It's not sure I can always remember all these three off the top of my head But I can just look up this spreadsheet and quickly add in my source Um, and I try to include as much information Given the given the fact that there's two purposes for this Purpose one might be I am listening back to um, this particular uh previous youtube video and I say I really got the audio the audio really came out nicely in that clip So therefore I want to include as much info as might be useful. So I can actually remember this particular one It was a flight Um that we took between Philadelphia and Dallas and there was some turbulence and I One of the things I picked up in the u.s. Was this action camera And it was before I'd figured out how to get the action camera out of its waterproof casing and so the audio was really muffled in it So that's the kind of thing I would just put in um internal Microphone while in waterproof case Uh, and you could even say the one that came was the product now. Um, clearly there would be other ways to do this You could keep an offline spreadsheet and by putting it in the video description Everybody is going to be able to see it now. The reason I did it like this is because um As I said, I might want to share these for critiques or feedback when I get a bit better You could do both you could keep a spreadsheet. Um, just so that would probably be the better approach in fact Every video keep a spreadsheet Keep your description in the spreadsheet. Keep production notes as a separate part of the spreadsheet and you can decide Um, if you want to share one particular production note with the public and then hide the rest of them But the way I'm doing it right now is just like this. So stabilization nil. This is if I use a gimbal or not or Um, in the future, I would love to experiment with steady cans and those manual ones Lighting and again, I'm just trying to be descriptive. This was cabin lighting only So it was variable because it was in an airplane and the cabin crew changed the lighting a bit So I'm not not just going to write, you know, artificial lighting or led lighting Uh post production is, you know, whether I do it firstly or not Some of the videos I put up on youtube are just raw and others I've edited and as he says, um, I would like to get to Da Vinci, um, eventually so that way I'll be able to just go back And see and look through a clip and say, okay, that was Caden live So I can kind of see the way the transitions were done. There was a bit different and etc So that's just one video Description and as he said, I go back through these in batches and I'll do sort of like 10 or 20 at a time Uh, I try to do this obviously as soon as possible so that I can still remember the microphone I use it's unlikely. I'm going to remember a year later Whether I shot a video blog using a lapel mic or using a shotgun microphone So that's why I try to capture that info ASAP post shoot um, okay, one final thing I do is You could call this my hacky maybe um a little bit, but um, I basically will I'm just going to go through and get open a playlist I thought I did this before I started rolling this clip, but apparently not Let's just go into this one. I just created a demo one for the purpose of this video. Um, so here we go So what I will do um, a lot of the time or sometimes is choose test clips around the house, um using my phone or um, edit them a little bit And these are just for me to test out a functionality. So I did one a few days ago using a microphone on a boom pole and testing around my apartment now. I didn't want to put that up publicly because of the fact that you know, it's my apartment And there's kind of a privacy issue there. So what I did instead was I've created um about five or say, I'm going to say 10 Probably five or six playlists and I call them production. Um, and what I have is a bit like this I have one main one called production I have production audio Production lighting production stabilization Production um Um production uh cameras and You know each one is uh, so that I can quickly Go into a playlist and it's going to be Test of a specific function. So right now I'm testing at microphones. So when I upload a private microphone test Um, I will basically uh tag it or add it to the playlist Production and production audio. Those are all private playlists But I have them bookmarked and that means I can just go in to a private playlist and listen to a few different tests I did Um, and that is basically it another thing you can do is create an alternative approach that you may prefer You can create a separate youtube channel Just for testing and then set the upload default On that youtube channel to private. So that's that's another thing you can do. Um, I've done that too, but I Find it's typically easier just to work Everything in the one channel But um, if you wanted to add different brand managers or you're you're burgeoning crew Uh to a channel but not to your main channel. Um, then that would be an option I think where that kind of a thing would make sense. So this is basically what I'm doing so far. Um, right now I'm excited and trying to Level up as quickly as I can but in the usual course of things I kind of Do this at a lesser pace, but um, I think all together between the Spreadsheet and the private playlist and the video descriptions Um, it's a reasonably good system for keeping track of all the different Technical aspects that go into producing video and you know from my perspective right now the ones I'm focused on are audio video lighting and Stabilization and that's and post production editing But I'm trying to get those kind of core technical skills down before I start doing more sophisticated things like interviewing people and etc because I did my first interview or one of my first interviews um recently and uh, well a few months ago and I didn't have the gear that I've just got in and uh The audio was absolutely unusable. Um, and that kind of taught me that I should probably learn get my Skills down and get my gear down Before I waste anybody's time having them sit down with me for 30 minutes to tell me an interview Only to have to write right back and email or facebook saying I'm really sorry, but I can't do anything with that audio. It's just like Not usable. So that's kind of my goal is to sort of avoid those Situations from happening again. So I hope this video is useful. Um, if you have your own uh methodology for YouTube videos and for um editing your youtube videos and making things better Then please feel free to leave me a comment telling me what you guys do Or how you how you think this system could be better. Thank you very much for watching And if you want to get more videos from me, please feel free to subscribe


Can anyone hear me online now? Give me a thumbs up if you can hear me. I think you can. Perfect, thank you. Okay, so sorry about this shuffle. We moved to a different room. Maybe that wasn't necessary because we have a number of people here and a number of people over there. There's a lot of competing sessions right now. But thanks for joining the session today. We're gonna be talking a bit about artificial intelligence and machine learning, how those kind of work in the DHS2 ecosystem. We have a few presenters who are gonna share some, AINML as well as data science, just advanced analytics. But I'm gonna do a quick intro on AINML and talk a little bit about how that can apply to DHS2 and the areas where we work. And then we'll have a few presenters who we'll share. So thank you all for joining us today. Hopefully we'll have a little bit of time at the end of the session as well for some questions and maybe ideas as well because it's a very quickly changing field with lots of things that are happening. So I'm very excited to see where this will take us. This has no significance other than that. It's a very cute picture that an AI drew. All right, so what are the opportunities for AI and machine learning in DHS2? So before I move to the next slide where I have a few possible answers to this question, do we have any ideas in the audience? I don't know if we can share this or I'll just repeat what you say. So did anyone in the audience or online have an idea of what would be a good way to use AI or machine learning or even not necessarily is but data science and advanced analytics in DHS2. Any show of hands, any ideas? Lars, data forecasting, very good. Any others? Other ideas? Yeah, interpretation of data points, maybe interpretation of visualizations or being able to kind of not just show somebody something visually but tell them what's interesting about what they're looking at could be a very interesting use of language learning models. Lars has a lot of ideas. Anomaly detection, that's a good one. Any others, do we have any online? Feel free to put it in the chat if you do. Sorry, yeah, it was a little hard to hear. Yes, tuberculosis. X-rays, yeah, X-ray interpretation for tuberculosis detection, absolutely. Image analysis, that's another good one. Okay, so there are a few ideas here. Let's see what ChatGPT says. So I actually asked ChatGPT this question and came up with some pretty good answers. They're not perfect. Some of them we've already heard here today but you can all follow this link when I share the slides as well. So I first just asked, how can AI and machine learning be used in DHI's too? And we have some data quality and cleaning. So that goes along with anomaly detection which is also down here. We have predictive analytics which is forecasting. We have decision support which maybe is similar to interpretation of visualizations but we actually also have that one down here as well. So data visualization and exploration and yeah, a number of other things here. And then I went on and I asked it, all right, what about in the software development process of DHI's too? How could we apply artificial intelligence or ChatGPT or the language learning, large learning models? And there's some good ones here. So data pre-processing, how do we, that one I'm not entirely sure if it knows what it's talking about but that's okay. Automated testing is a very big one that I think is very much worth exploring. So making sure that software is robust. We don't have regressions. That's something that humans are not that great at, right? You can throw a lot of human power at it but it takes a lot of time and you're gonna miss some things. Whereas a machine is very good at figuring out all of the possible paths for something. Obviously code optimization, documentation generation is a very good one. So being able to say, all right, we know what this does. Now let's describe it and let's help people to figure out what's going on. Obviously bug detection and resolution. And then I went on and asked it again. All right, so what about user training or documentation and support? And we'll learn a little bit more about this later. And so user training is something that chat bots or virtual assistants could be very helpful for contextual documentation. I think this is actually, and I recommend talk a little bit about this later but I think this is a huge area of opportunity where we have generic documentation and we have very configurable DHS2 systems that are maybe difficult to kind of, for an end user especially to take the generic documentation and their specific configuration and put those together and figure out how to do what they want with the configuration of their system. And so if you can then take that documentation and contextualize it to what their use case is or how their system is configured and present that to them, that could be very, very useful. And also do that in an interactive way so that they don't have to search through a huge document to find what they're looking for but they can ask the question that they want. Troubleshooting and support, recommendations, I think is a good one as well. Another one that didn't come up here but I think is worth exploring as well is being able to do intelligent mapping of different configurations or different metadata in different systems and automatically try to figure out how you use something in one system that's called something slightly different than another one. We had a presentation from Pete Linnigan from BAO Systems the other day who used a pretty lightweight but a model to say within a certain threshold, let's find names of different data elements in two different systems that are similar but not exactly the same and suggest that that mapping should exist. So if it's called, I don't know, ANC in one and it's called antinatal care in the other, it can say that those are probably pretty similar even though it's not an automatic mapping. So this is basically all to say that there's a lot of opportunity here. Obviously some of it is hype and we're not just going to throw everything out immediately and jump on the AI train but I think they're especially with the recent developments in AI machine learning, advanced analytics we didn't talk about as much here but there's a lot of opportunity there to really supplement and support the processes that people are using DHS to work on. So that's all of my intro. I just wanted to give kind of a fun little dive into chat GPT, we could explore this a little bit more and maybe I'll let, oh wait, I have to log in with my account, that's over there. I'll do that another time. So maybe if you can think about the next question to ask kind of beyond this, maybe to dive into one of these specifically, we can ask that at the end if we have some time. But with that, I think I'll turn it over to our first speaker who's from Solid Lines who's going to present a bit about data science or advanced analytics with DHS2 data and then we'll talk a bit more about machine learning and artificial intelligence in the following presentations as well. Thank you. Great, I hope the people online can hear me. So I'm gonna be presenting an integrated architecture for adding analytical power. My name is Sakura Lopez and I'm from Solid Lines. So we won't be speaking too much about ML and AI applications but more of an architectural approach that can allow you to do this type of analysis. First thing first on the agenda said that Carlos Tejo would be presenting this. He's one of our lead software architects. Unfortunately, he couldn't make it. I'm presenting on his behalf. I'm a data analyst and BI specialist at Solid Lines. So I will do my best to try to answer your technical questions afterwards but I may have to send some of those his way after the presentation. So a little bit about us. We are a small digital consulting company based in Spain and we do a lot of different projects from server management to DHS2 implementation. We have 15 plus years of experience working across 25 different countries but recently we've really been interested in looking at building analytic platforms helping organizations build their analytic platforms. So today we're gonna look at some of the DHS2 analytic capabilities, what it looks like to scale that architecture and the strengths of that architecture and then some use cases from some of the partners that we work with. So first, DHS2. There is a lot of analytic capabilities in there as you guys know and the functionality is improving with each release. And what we've seen with a lot of our partners is that most of their use cases could be achieved through DHS2. I had one partner come to me and say, oh, I want you to help me with this Power BI report only to spend many hours figuring out we could have done it all in DHS2. So we wanna prevent that. And the key takeaway there is that you should have a really strong use case if you're gonna pull that data out of DHS2 and visualize it in a BI tool. And usually it's DHS2 is the best option when you do have all of your data in one DHS2 instance. But there are those use cases that have really pushed the boundaries of what's possible with DHS2 in terms of analytics. So some of the things that we've seen is people wanna build those elaborate dashboards for their donors or a public facing dashboard. Maybe they have really massive data sets or they're trying to do an analysis of complex indicators and heavy data transformations. They may wanna combine data from multiple DHS2 instances and often they wanna triangulate their data with external data sources or unstructured data sources. So this can range from chat bot, free text conversations to weather conditions to system blogs. So what does it look like when you want to scale that architecture? So as I said, a lot of the things that people wanna do can be done in the dashboards and the visualizations but sometimes people need to pull that data into a BI tool for more elaborate visualizations and complex analytics needs. So when you need to do more complex transformations and then a benefit of that is you're able to pull in other data sources. However, there are limitations to this approach of importing data directly from DHS2 into the BI tool. And when that's the case, then the organization may want to consider integrating a data repository and this is for more advanced complex analytic needs. So your data repository is comprised of data lakes and a common data warehouse which we'll delve into in a bit. But first let's talk a little bit about the difference between pulling it directly into a BI tool versus using this data warehouse solution. So a BI tool, you would go through these various steps to do some kind of data analysis and then facilitate data use. And you can do this with an array of tools, right? We had a presentation on SuperSet yesterday. There's Tableau, Power BI. However, when you have these more advanced analytic use cases, then you may wanna consider using this data repository for the first two steps. So to connect and transform the data and model the data. And a key point here is that even if you use this data repository, you're still gonna have to pull that data into the BI tool to visualize it and share it with your data users. But if the organization decides that they want to implement this integrated analytics system, it can be done on-premise or using cloud computing platforms like Azure and AWS. If you do it on-premise, it is a lot of work and it's quite expensive. Within our own implementations at SolidLines, we are using cloud platforms. So that's what I'll be speaking to mostly throughout the rest of the presentation. So as you saw on this slide, both BI tools and the data repository can do similar things, right? They can both connect and transform the data and model the data. So why would you choose to incorporate a data repository? And there are several reasons. The first that we've seen quite a few times is that when you have people pulling a lot of DHS-2 data, a lot of API calls really often, it could stress the server, which you really want to avoid if you have a large DHS-2 instance across multiple countries where there's a lot of users. It also allows you to rapidly access huge amounts of data. It reduces the dependency on the availability of data sources. So for example, things like Facebook, they'll prevent you from pulling historical data like five years in the past. If you have it in a data warehouse, it's going to be in there for you to analyze until you remove it. It also provides the point of truth for multiple users, like especially around your common dimensions. So what we mean by this is for data sets that people are using often in analysis, like your org units, it can be in your data warehouse and people can pull that over and over and over again and they don't need to do the transformations themselves. It also structures data in a way that makes it easy for BI tools to analyze. And you can do this with any BI tool, right? Like open source BI tools, commercial BI tools. And lastly, it really helps organization scale. So with regards to the cloud platform, scalability is related to being able to store and manipulate significant amount of data from any source, structured or unstructured. But the cloud platform also facilitates with transparency and consistency. So you have core data consistency for those common dimensions like maybe your org unit tree or a calendar table. You also have a collaborative approach to data transformations. So for that final integrated dataset, it's very easy to see what transformations took place to get to that final dataset. Also facilitates data security. So your data is distributed across multiple nodes. And then if there's ever failures, you can store those failures for further analysis. And there's ways to do safeguards for restricting sensitive data and PII. And it's cost effective, especially when you end up having huge amount of data, the storage cost is quite low. And it eliminates silo costs in the sense that people aren't performing the same transformations over and over. One person can do the transformations and store in the DWH. And then it's there for all of your analysts to pull into their BI reports. So what does this architecture look like? So first you have your data sources and those sources are extract, the raw data is extracted through a pipeline into the repository, the data lake repository. The data lake repository has three data lakes. This is typically the best practice for the infrastructure. The first is a raw data lake and that's where you have your data that is in its original format. Then you have a curated data lake and that's where you have the data that has undergone transformations. And it's also where you can remove sensitive information and PII so that users cannot access that later in the warehouse. And finally you have a staging data lake. So that's where your data is stored and it's an analytic sandbox where your data scientists can use that data to perform deep data analysis. So then you can store those integrated data sets into a data warehouse, which we call DWH often. And again, that's done through a pipeline that's pushing those data sets into the DWH. And the DWH provides the structure for that data. So usually this is done through dimensional data modeling. Once you have that data modeling that data can be fed into a BI tool to visualize it. So what types of data do we see being put into these tools or into this repository? So most often it's chatbot data, social media data like RapidPro, demographics, weather conditions, server logs, free text conversations. This can range from like comments on YouTube to Facebook messenger. There's also a lot of data from databases themselves. So DHS2, FireMoodle, Matmo, all kinds of things. Possibilities are really endless. So we're gonna look at a couple of use cases from one of our biggest partners, Population Services International. This is from a project called DISC and they're pulling data from over 30 countries and multiple data sources. So chatbots, social media, fire, system logs. And in this particular part of their Power BI report they're looking at ways that they can facilitate followup for people who are doing self-care in reproductive health. This is another example where they are pulling data from multiple DHS2 instances to look at health quality care within the private sector across many countries. So once you have this architecture in place, how would an organization engage with it? So in your data lake repository, this is where your data engineers typically configuring the system. So the structure of the data lakes, the DWH, configuring the pipelines that really make it all work. Then your data scientists would typically be doing the big data and deep data analysis within the data lake repository where you have all that unstructured data. Within the data warehouse, your data analyst is querying that routine data but they're also using it to develop reports within Power BI so that hopefully eventually the data users can use it to gain insights and make informed decisions. So what is driving the analytical power behind this type of architectural approach? And in our opinion, it's two key things. The first is the design principles behind the architecture. So first data lake structure where you have three different data lakes for your raw data, your curated data and your staging data. Second is common dimensions and dimensional data modeling which we didn't get to go into too much but it's one of my favorite topics. So if you have more questions come up after I'm happy to talk more about it. And then reusability. So in terms of the architecture, the pipelines that are built can be reused but also reuse ability in terms of getting the data over and over from the data warehouse because it's there for multiple data users to use. And then on the technology side, we have Hadoop. So HDFS and this is a highly fault tolerant distributed file system and Spark which provides the computational power for transformations over large distributed data sets and both of these are open source. So for our final use case, we'll look at usage analytics and this is also from PSI. So they were interested in understanding how people are navigating the system, how visualizations are being accessed and whether the dashboards are really being used. And there are capabilities within DHIS too that allow you to analyze favorite views and top favorites. However, if you pull the system long data into a data repository using the data lake and DWH approach, it gives you much more dynamic analysis capabilities, a lot more flexibility in what you can actually analyze. So the possibilities are really endless. You can look at things like API endpoints used, the latency times, the devices and browsers that are accessing the system, conflicts and errors during synchronization. So what would this look like in practice? The data is being pushed to the data lake where it's stored in a file system and you can push that data as often as possible you set that schedule. In this case, it's being pushed on a daily basis, the log data. This is what the raw data looks like in the raw data lake. Then it goes through the other two data lakes so it gets curated and put into the staging lake. And then it's modeled in the DWH and this is what the model looks like once it's brought into Power BI or the BI tool. So once you have those core transformations and the modeling, you can end up with reports that give you some very intricate and detailed analytics. So for example, you can understand the number of the users in the system, the size of the data, the number of API calls. You can look at how many calls were successful versus how many failed. And you can also get a better sense of who is accessing the system, how often they're accessing it. You can even go as granular as seeing which dashboards, which visualizations they're looking at. So key takeaways from this presentation. This architectural approach is definitely an investment in terms of time and resources. And it requires buy-in at the leadership level. This is not a project-based approach. You really need to do this at the organizational level. But as organizations, data size and complexity increases, you really need to think about the limitations of connecting the tool directly to your BI tool, the data directly to your BI tool. For the architecture, it can be on-premise or in the cloud, but cloud solutions typically provide better functionality for scaling and security. And in terms of the powerful aspects of this architecture, it's driven by the data lakes in DWH, which facilitate scalability and data processing because they provide high data availability and fault tolerance. But, and you can also have really powerful analysis of the data, but you have to make sure that it's properly modeled. So thank you so much. So is it next? Does anyone have a quick question while we switch speakers? Great presentation, thank you. I'll repeat your question. Did you hear the question? So I'm going to repeat it for those online as well. I'm actually going to have you repeat it. I don't, I don't miss a chapter, please. That's fine. Since you are pulling data from different sources, if you have the same data, say from the population data from two different sources or stock data or something, then how do you reconcile which data to take and which to ignore? So you're saying that if you had two different data sets that are both around population data and you have to decide sources, I think that would really be dependent on like the organization and what they decide is the most credible data source, for example, or what's the most appropriate for their use case, like maybe what's the granularity of the data set and is that useful for what they want to end up visualizing in the end. So credibility and then aspects that influence how you could apply that data, like how granular the data is. So basically the data analysts will make the choice and which data to choose if it is coming from multiple sources. Yeah, so I think from our implementations, typically like we would have a discovery phase where you look at the various data sources and you try to understand how that data can be pulled into the system and how it would be visualized. But typically, at least in my opinion, you always want to understand like what the end goal is and what actually needs to be visualized based off of user needs. So when they're looking at that data, what kind of actions are they supposed to be taking? And then that should really drive what data source you choose, right? Because the data source needs to be able to provide that kind of functionality for them to make informed decisions at the end of the process. Go ahead. Hi, Sean Brumman from HIST South Africa. So really just a comment, I wanted to say thanks very much, this is fantastic. We as an organization prioritized data science about two, three years ago. And if we had seen this presentation, then it would have been incredibly helpful. So I think anybody that's wanting to go down this journey, this is a fantastic cheat sheet. And we spend a lot of time learning lessons which we would have saved if we'd seen this presentation. So thank you very much. And those who want to see what we've done, we've done a production level national system that uses machine learning to predict staff attrition in South Africa, which we're considering in the next session. Very cool, thank you. I think we do have to move on to the next speaker, unfortunately, I'm sorry about that. But we did a one question in the back, so maybe you could talk to him after. But yes, we'll move on now to, yeah, Kayla who is going to talk to us about application of machine learning with low code. Okay, hi everyone. I want to thank the last speaker as well. I think that that was a great presentation and something that we're also struggling with and hoping to improve upon in the future. And it's very relevant to what I'm talking about. So as mentioned, I'm going to talk about an approach that we are using for machine learning with our DHIs to tracker data. And I'm a technical advisor in health informatics and data science at FHI 360 where I work on a large HIV project. So Austin kind of stole my thunder. I also asked chat GPT some questions. I'm wondering who's all used chat GPT before? Yeah, a lot of people. I think AI tools are becoming more and more available and something that I hear people, especially people working in analytics, say often is, oh, AI is going to take our jobs. So I thought I would test that hypothesis and ask chat GPT to write me a little intro to my presentation. And what I came up with was actually a knock, knock joke. So I'm going to tell the first AI generated DHIs to knock, knock joke. So please, please help me out if you know the familiar or the format of knock, knock jokes. So knock, knock tracker, tracker capture your attention. DHIs too is here to help you track and manage your health data. So I hear some clapping, but not necessarily a lot of laughing. I would argue that this is not a very good joke. And I included this because I think it's an example of the limitations of AI. And I think if you take away one thing from this presentation, it's that while we can automate machine learning, the most important part of the process is everything else that goes into it, right? We still need to collect our data, clean our data, format our data as the last presenter mentioned in a way that can be analyzed using machine learning. And then we need to take the results of that model and apply it to something that actually matters. So I would argue that the automated machine learning part that I'm talking about is really a very small piece of the puzzle. So with that context, let me go a little bit into our approach. So in general, this is the supervised learning process. If you're familiar with machine learning, we start with data acquisition. So we get data from the source and try to understand our data. Next, we do data cleaning and something that's called feature engineering, but it's essentially looking at what parts of our dataset are we going to use to try to predict our outcome. We do the actual modeling and then we deploy that model to new data and put it in the hands of the people making the decisions based on that prediction. So the way that we're doing this is with a combination of DHIS2 and some available software through the Microsoft Power BI platform. And I'm going to give you a demo of this. I have some more detailed slides, but I don't think I have enough time to talk through all of them. So if you're interested in more information, feel free to reach out to me, but I'm going to give you kind of a practical application of how we're using this. So this is one of the use cases we're looking at HIV in Lesotho. So Lesotho is a country in Southern Africa and it's estimated that around 290,000 people live with HIV in Lesotho. It's thought to have the highest prevalence of adult HIV in the world. So we run a community-based project in Lesotho in 12 community councils and we work with participants who are at high risk of acquiring HIV, female sex workers, men who have sex with men, transgender people and priority populations. So we collect data in Lesotho for our community-based project using DHIS2 Tracker. And this is just an example from a mobile phone of the kind of information that we collect. You'll see we have a number of program stages and many of the stages are collected by different people. So when a client is reached by our project, they first get a risk assessment from a peer educator. They're then offered HIV testing services and an HTS lay counselor provides those services. If they test positive, they work with a peer navigator for treatment. And if they're interested in prevention via pre-exposure prophylaxis, they work with a prep nurse. So all of these different stakeholders use our system to collect data. We've been using the system since around, I think 2021, but we actually back entered data. So we have about three years of data in the system right now. So why did we want to use machine learning in this context? I think some of you are probably familiar with the 95, 95, 95 goals, which is quite a mouthful. But essentially they're global goals where we're looking at trying to ensure that 95% of people living with HIV know their status, 95% of people are on treatment, and then 95% of those are virally suppressed. And this is what that looks like for Lesotho. They're actually doing a fairly good job. So we wanted to look at, can we use machine learning to prioritize HIV testing and reach the undiagnosed? So we're using this really with a subset of our population, or this is the thought is to look at people who we've reached, but haven't been tested for a number of different reasons. And ideally we would follow up with all of them, counsel them and try to get them to come in for testing. But if we don't have the resources to do that, how can we prioritize that time intensive follow up to those most at risk of being positive? So now I'm gonna give a quick demo. I hope this works. Okay. So I just wanna highlight some of the features of the approach that we're using. So we do not have a data lake that's bringing all our data into the model. That would be great, but instead we're going directly to a BI platform. So this is Power Query in Power BI data flows. And I just wanted to point out, so this is our data model and I'm not going to go into all of it, but we've parameterized the model. So what that means is that you can just change this information about in the system and then you can refresh the model and bring in data from any DHIS2 instance from a program. So we work in, do you say 35? But we work in very many countries. I think we have 18 countries with DHIS2 trackers right now. So we wanted to make an approach that we could replicate. I think a lot of you probably know it's sometimes very challenging to get data out of DHIS2. So we parameterized this model so that we could easily replicate it across countries. So once you save your data model, this is kind of what the system looks like. And I just want to show you how easy it is to apply this auto machine learning. So you just go to machine learning models right here and you can see that I already have a trained model here. But if I wanted to add a new model, I just press that add ML model. I choose the table that I want and then my outcome. So I have a lot of data here, but I'll scroll down to the bottom and choose test results and then press next. All right, there's a trying to get to my next tab. Okay, so once you press next, it actually takes a minute to load because it's analyzing what that column contains, but it gives you options for the kind of model you can run. So you'll see regression is grayed out here. It's because it knows that this is not numeric data and not appropriate for regression modeling. So I'm just going to choose binary prediction. And it has you choose your target outcome. So I know that the results I want the system to predict is positive and that's how it's stored in my data set. And then it just has you specify a match label and a mismatch label. So instead of not positive, I'm just going to put negative because everyone who's not positive in our data set has tested negative. And then this is really the important part. It allows you to select which features you'd like to use to try to predict your outcome. So again, it's not a magic system that can automatically understand everything about your data. This is where the human element really comes in. So I'm not going to train this model right now. Let me just select like one thing.  You can have the model train itself for up to six hours. I've tried that before. It never actually takes six hours. Maybe if you have more data, it would. But after you press save and train there, and I think this is one of the really powerful parts about AutoML is that it gives you this really great training report. So it's going to give you some basic model performance statistics, a confusion matrix, which I won't go into any statistics here, but you can see visually what your top predictors of your outcome are, if it loads, and then you can actually dig down and get more information about each of your predictors, which is really helpful in terms of understanding how the model is making its calculations. You're also able to fine tune your probability threshold. So again, I promise not to go into statistics, but this is incredibly important when depending on what kind of outcome you're looking at. So since testing HIV positive is a fairly rare outcome, but we don't want to miss anyone. We want to ensure our recall is quite high. So we're able to identify everyone who is positive with our results. So again, I'm happy to go into more information for anyone who's interested, but I'm cognizant of time. So I'm just briefly going to go through some results. So we applied the model with a probability threshold of 0.35 to around 8,000 people reached by the Epic Project and it predicted that a subset of them could be positive. We haven't fully tested all of this yet, but we've reached a subset of the population identified by the model and we were able to identify new positives at a higher rate than our regular testing. And we were able to identify some new positives, which I think in any case means that this was a successful pilot. So there are a number of limitations to this approach. I could talk for this about limitations just for 15 minutes, but there's limited ability to fine tune the model. Changes in the model take significant time. Power query, while very powerful, is also very slow in some cases. And it's difficult to replicate the model via program rules. So Power BI or AutoML gives you a lot of details about how the model is made. And it's actually based on Scikit-learn. So it could be replicated in Python, but it's not simple enough to replicate directly in a DHIS2 program. So just back to chat GVT briefly. I wanted to give it a chance to redeem itself because its joke was not very good. So I asked it to write an ending to my presentation and I actually kind of liked it. So I thought I would read it here to finish us off. So it said, in conclusion, the combination of Power BI, Microsoft AutoML, and DHIS2 tracker can enable advanced analytics with minimal coding. While automation plays a role, our human understanding of data sources, systems, context, and model evaluation remains essential. Let's embrace these tools, harness the power of human intelligence alongside machine learning and unlock new realms of innovation. Thank you for joining me today and may your data-driven journey be filled with endless possibilities. Thank you, everyone. Thank you very much, Kayla. I think the point that you made a couple of times is really essential that your predictions or your analysis is only as good as the data that you're basing it on. And you really need to, that is not an automated process to make sure that you have quality data. You can use automation to help with it, but it's still a very human process and making sure that you are training things on the right data is very important. Okay, next up, we have a very interesting presentation. I'm sure many people have questions for you, but we don't have too much time. So you saw what you looked like, come up and speak with her afterwards. Next up, we have a very interesting presentation on documentation for DHIS2 and how to use some advanced machine learning and artificial intelligence to enhance that. So I'd like to introduce Irik from our tracker team. Yeah, so I trust you all can hear me. There has been three great speakers performing today. They all mentioned chat GPT. So you can probably guess what I'm going to talk about. Okay, I think I should be at least, hopefully I'm not sharing to the other room now. Oh yeah, there you go. All right, so for those of you who don't know me, my name is Irik. I'm a developer on the tracker. I'm part of the core development team here at the University of Oslo. In addition to being a developer, I'm also a project lead where we use DHIS to try to track climate emissions for the private sector and businesses in Norway. So for the last two and a half, almost three years, I've both developed and used DHIS2. So I know your struggles. I know your pain points. Today I'm mostly going to talk about LLMs or large language models that they're called. Large language models itself, the term is pretty much unknown. You're usually known it by chat GPT or just GPT. But before we go that far, we need to make sure that everyone's on board on what actually LLMs are. And I use the LLMs and not chat GPT because there's multiple. It's not only open AI and there's much more to choose from. So what are LLMs? And LLMs are basically just huge, complex, advanced AI models. The same AI models that we're sort of getting used to when we're scrolling our social media or buying products on Amazon. The only difference is that this AI model is trained on a lot of conversational data. So it's trained on billions and billions of basically just text. And it sort of analyzes and it can actually get the context of the text that it's working on. And that is the revolutionary part compared to just having like an auto complete on your iPhone or whatever. So I talked about chat GPT and chat GPT has gotten a lot of media attention in the last couple of months. Usually it's painted this dark and gloomy sort of picture of how we are not connected anymore and students don't teach anything. And I brought two samples up here and this was just the last hour before making this presentation. So there's a lot of dark and gloomy stuff. And the first one is chat GPT founder, Sam Altman, who was quoted on something that learning is going to be a bit different. I read the article and they paint this picture of students not having to learn anything, they don't need to go to school. And of course, that's not the reality. The other one is actually a pretty fancy one that was in Tecto Dano, which is a famous Norwegian technology magazine. And it's the very first all AI church service. And you can see the screen up there. The reverend is created by AI. The preacher was a 40-minute preach basically written by a chat GPT. And there were over 300 people attending that class. So almost as much as the annual conference itself. All right, I've talked about it trying to analyze and actually getting the context of the text that is trying to analyze. And I'll give you a live demo on what we've done. We've tried to improve the documentation of DHS. Before we go there, like show of hands, how many people have entered the docs of DHS in the last year? There's pretty many, there's pretty many. Typical use cases just to do that is often like creating SEO content, like content that's good for Twitter and Google and everything. It's writing essays in long texts and also translating languages. We've taken a different approach and this is just like a brainchild of mine. It was very cold in Norway and a lot more cold than it is now. And the benefit of it being cold is that you get a lot of hours to think around your computer. And what we did is that we used GPT or I did, I used GPT and tried to take the docs and splitting up and I have some technical jargon that is not really important, but I'll try to show you what it really is. And hopefully you can see it up here. So this is just a normal, it's just using the GPT model and you can pretty much ask it anything related to DHS. So we'll try what is a data element. And once you write it, we'll check this vector database in the backend and we try to get the context of the user query, match it and get the relevant context from the documentation and send it to OpenAI so they can rephrase it. So it seems like it's a personalized person trying to respond to your question. We can also, oh, sorry, sorry, sorry. Better? Okay, yeah. Okay, so we can also do this on more complex tasks. That's one of the benefits of using LLMs is that it can break down pretty complex issues and give it to you in a format that's personalized for you. So we can, there's been some people talking about how to use R with DHS. And we can sort of tell it, can you give me five reasons to use R with DHS? And we'll go through the documentation on how to use R, what is R? And we'll give you five good reasons or at least five reasons. We can also rephrase it, say that we want to print this thing and actually show it. So we can say, can you show me in a table? So you get in exact format that you want to get it back. And this is, of course, using OpenAI, so it takes some time, but there you go. And it's back to showing you in a table that you can now display to others. Of course, this doesn't always make sense. But it's more of the having the option to actually do it. Another thing about using your own separate application is that if we go to the OpenAI sites and we just use the GPT-3 and say I'm a developer and I want to check out what are the new endpoints in the tracker endpoints, which are no longer new, just a shameless plug that you should move on from the old ones. What are the new endpoints in the tracker? Well, that's GPT. I apologize. Of course, he doesn't know. He knows a lot about DHS in general, but the new endpoints in the tracker is so new that it's not been trained on this data yet. So what should we do? Should we wait for the new GPT-5 or whatever to come out and actually be trained or should we provide it ourselves? So let's ask the same question in here. What are the new endpoints in the tracker? Sorry for my spelling. I will try again and it analyzes the context of the question and hopefully it returns five new inputs. Thank you. I'll also very, very quickly show you how to do some reporting and analyzing data through GPT. I know not everyone likes to do it this way, but it's more to give you an idea of how easy it is to get started with these kinds of things. So I have a visualization in the data visualizer app. This is part of the Sierra Leone and it's basically just ANC-3 coverage for the last 12 months. So I choose the same visualization. I'll go down to this one. I won't enter a custom prompt. I would just say generate a report from this data and this is finalized data and it gives you a report. And it starts off saying, what is the data? It just goes through saying that this is data for the last 12 months. And it also wants to give you a huge overview of all the data and everything. So it's pretty good, but it's not good enough. So how about if we do the same thing and we provide it a custom prompt, let's say include a title, key in slides and actionable points and generate. I see that I'm running out of time. So I'll just try to go through it. But here you have the same visualization and the only thing that we pass in is basically what we get back from the API. We don't do any salutations. And here you have some key insights. Ah, sorry, sorry. Once again, I tried to read your lips, but I'm not good at that good. All right, so it tries to go through and it says it's for the last 12 months from June 22 to May 23. And it says which district it's on and the district wise performance. It tells you the minimum, the maximum and the average.   And the actionable points is actually pretty good. You can, let's see, this is of course live data. So I'm not really sure what it says back, but it's monitoring performance, investigate data quality issues. And it's not only just saying that you should investigate these kinds of things, but it's saying that in Fujihun, a specific region, there's something that might be wrong. And this is not replacing the great stuff that the people before me have talked about, but it's more of a giving an indication and really, really easy trying to see what can be done. So these use cases are of course not viable. They're not ready for production. They're not ready for anything, but I'll just tell you this to conclude everything. If you were to make this app like the old school way with just code, no AI, does anyone like to guess how long you would have taken? I can tell you now, this is under three hours of development. So it doesn't take a lot of money. It doesn't take a lot of effort. It's really simple to get started. I don't know the use cases, but I'll show you. And in true DHS fashion, I'm just expecting you guys to go wild with it. Yes. All right, I'll end there. Thank you, Eric. We are wrapping up now, so it'll be time for the coffee break here in a minute. But I did want to touch on something that a few people have said. I think Eric mentioned education in particular and people saying that AI is going to destroy learning, but there's actually a pretty good talk recently by Saul Kahn of the Kahn Academy who talks about that it's actually the opposite of that. Especially conversational artificial intelligence can give everyone a personalized tutor that understands their context and helps them to do their job or to learn something new and helps them to understand it better. So being able to kind of evaluate a visualization in DHS too, but also being able to say, all right, with the configuration of my DHS2 instance, how do I do this? That's something that if you have a thousand users, you would need a lot of people to be able to answer that question or to spend a lot of time training them. And so I think training documentation and analytics of things within the public health context and within DHS2 is a big area of opportunity for us. Thank you all for joining and everybody is around if you want to continue to talk AI and machine learning and advanced analytics in the future. So thank you to the presenters again.


The tides and currents of the great ocean are many, and frequently surprise, but of all the flotsam and jetsam fate has flung across my bow, you definitely rank amongst the more curious. I am unsure how to respond to that. My, not off to the best of stars there, aren't we? I had hopes for more of a conversation, but then I suspect you do not want to admit you are a little bit new to this. I will have you know I have spoken with many of- Oh, spare me any protestations of ability or virtue. Your racing heart and ragged breathing tell a different story. I do not need the great ocean to know you are quite clearly out of your depth. I frankly care little what you think of me, Heretic. I was wondering when that word would first be deployed. Not wasting any time, I see. I hope you are not one of those dull types that believes it carries any sort of weight. It is boring, honestly. How quickly it arrives in every interaction I have taken part in here. And here I had hoped conversing with someone from outside the Ordos might soon liven the tedium. This is an interrogation, not a conversation. Lies already? No, my friend. This is a conversation. You are in no position to dictate anything to me. No one has been since I arrived. Since you are captured. An odd way of characterizing a willing surrender, but call it thus if it makes you feel better. We discuss these attitudes prior to my departure. To cry to the sea that roared to us, to sigh, to the winds whose pity sighing back again did us but loving wrong. I beg your pardon? Shakespeare. Day tempestus. He wrote more than three plays, you know. Your records state only three, but they are wrong. You have forgotten more than you can possibly know. That is my first lesson to you. I am not here for an education in such matters. You remain at the sum total of the Inquisition's mercy, Iskander Kaon. Your life is forfeit. I hear that quite often these days, and yet I remain quite steadfastly alive. Do not pretend to be unaware of your valued servants of the throne. Then you must do likewise. None of this your life is forfeit nonsense. I beg you. It's so tedious. They will kill me when they think there is no longer any chance that I will unwittingly let slip some hidden plan or unseen weakness that will allow them to snatch triumph from oblivion's maw. Until then, here I dwell, and I assure you, I have much more to say. Your masters are fully aware of this. I do not serve the Inquisition, sir. Oh, that, my friend, is painfully obvious. Your two... I'm not sure. Something of the middle ground between weakness and righteousness, but bound by inquiry, not zealotry. But interesting concoction to find here upon terror, I will admit. Is this sort of charlatan cold reading routine intended to unman me? Perhaps. Or perhaps I'm just having fun. There's little to occupy the mind down here, and despite being a good listener, thought has never been one for chatting. Whatever your ill-will or insidious intent, you know as well as I that fear and suspicion, however wielded, at least served to keep one sane and alive a bit longer in this world. I shall keep a firm hand upon both. A fine creed. Iscander Chaon called Chaon the Black, called Kingbreaker, formerly of the accursed Black Legion, formerly of the 15th Legion Thousand Sons. Heretic Astartes, Tratoris Ultima, compatriot in damnation to the Warmaster of the Arch Enemy, Abaddon, called the Dispoiler.  So that in service to the God Emperor, you may make yet some scant recompense for your sins, which are legion beyond utterance. Ah, what of Giliman's little chroniclers? I was wondering how a civilian managed to get all the way down here. My hosts are not exactly known for their welcoming nature. I had heard that your ilk resurfaced since the avenging sun's rebirth.  To an age none of us will ever see again. But, yes, yes, I acknowledge, I stand opposed. Or hang, as it were. So you are to provide the Logos with an account of the current disposition of the Black Legion, as well as sundry information pertaining to its present relationship with other formations of Heretic Astartes. Do you accede? Does this lip service to the leximperialis bring you comfort? I'm surprised they would even pertain to me. Essentially correct. I mostly wish for you to understand the terms of this interrogation. Ah, conversation. Our records require updating. You are a source. An unforgivable one, but these are trying times. The Lord Regent has extended to us his directive that we ignore not in the preparation of material to better aid the endometous crusade. As Akail always said, Giliman was thorough. Fatenating he could manage to remain so, given how the chains of this rotten carcass of the realm he once helped build have so shackled him, I can't help but wonder what he felt upon seeing terror again. Seeing all that it has become in his absence, horrid, disgust, no small amount of professional insult, I'm sure, given his predilections, it's also woefully inefficient. The thoughts of the Lord Regent are none of my concern.  I have been nothing if not forthcoming, Starter. Are the tales of the Black Legion's genesis a bit too antiquated for your purposes? I am happy to speak to my brother's more recent activities, if you wish. I am not here to indulge in another of Iskander's Khaon's bizarre adventures. My goal here is the truth, Heretic, as it always is. You can save whatever fanciful tale is waiting upon your tongue for your next session with the Inquisition. Fanciful? My friend, I have told no lies since my arrival on terror. Do not insult my intelligence, Khaon the Black. I have reviewed your accounts to the Ordo in detail, and fanciful is, candidly, a generous characterization. Oh? Illuminate me, Oculus Investigatus. You claim that you sought the Arch-Trader, Ezekiel Avedon, former First Captain of the 16th Legion, Sons of Horus, at the request of another former Captain of that Legion, Falkus Kybra, called Widowmaker. You describe in extensive detail your travels with the Widowmaker across the Great Eye. And? My record states that Falkus Kybra died here, upon terror, during the siege. By the account of none other than Nathaniel Garrow. He died, Khaon. Did he now? Yes. During the Saturnine Gambit no less, 25th of Quintus, 014 M31. His death was witnessed by over a hundred members of the Imperial Fists, and other loyal legionaries from across the Imperium. Do you have nothing to say? What do you expect me to say, Storitor? That the 10,000 years I've spent at Falkus' sign, the battles we've fought and the brothers we've lost are... What was the word? Fanciful? Well, I expect you to admit to the Child's Fiction you've been spinning to throne knows how many eager ears. The universe is a strange place, my friend, and even the wisest among us cannot truly comprehend the totality of its mysteries and twisting paths. At the end of the day, I have my account, Garrow and the Fists have theirs. The task of passing the truth falls to you.  Patronizing condescension now, your fortune teller analysis was more effective if only just. So you have some spirit after all. Surprising, especially here. But come now. You did not come all the way down here to debate the historical merits of my accounts. Well, despite myself I did not. What do you wish to know? I wish to discuss the Dispoiler. But of course. Not his activities, mine, but his character. Many aspects of the account you have rendered thus far, as I have said, are suspect in the extreme. But some elements can be corroborated. Most notably, your account of the Dispoiler's character and temperament leading up to the first Black Crusade. That has been confirmed in detail by the interrogation of your former comrade, Sargon Eregesh, prelate of the Black Legion, scion of the ever damned word-bearers. Ah, Sargon. I did warn him that Flotellos weren't the most poorly laid traps I had seen in some time. But that's always the trouble with zealots, isn't it? Their ability to deny reality is limitless. When said reality conflicts with whatever they believe destiny has revealed to them, the interrogators of the Ordos must have outdone themselves. They managed to extract anything coherent from him. Well, coherent is hardly the word I would use, but his account does corroborate much of what you say of the Dispoiler in this period, even if the word of two of your ilk isn't that much better than one. What I seek to understand is his shift. The stoic and principled commander described in these records as a far cry from what Ezekiel Abaddon has become in the millennia since the Black Legion's genesis. Ten thousand years is doubtless a long time, even accounting for how much time must work within the eye. But describe to me how he transitioned from the figure described in your account into what he is now, the Archfiend of the Nine, Warmaster of the Great Enemy, Champion of the Primordial Annihilator. Your pardon, friend. I have been down here for quite some time, so some conversational subtlety has maybe lost on me. Is that a joke? I assure you it is not. Then I'm afraid I must plead ignorance. I do not find this feigned coiness convincing. I feign nothing. The question is ludicrous. I am sorry to disappoint you, but my brother Ezekiel is as he has always been. Do not take me for a fool, Kaon. Do you honestly expect me to believe that he remains unchanged after ten thousand years? Unchanged? Not hardly. Like every member of the Nine legions, Ezekiel has been broken and remade more than a few times during the Long War. But his true character has endured through it all. How else do you think he could command the loyalty of both gods and men? Explain. It began with Horus, of course. They destroy us, our fathers, in their own way. Horus made Ezekiel. Both Ezekiels, mind you, the first captain of the 16th and the current warmaster of the Pantheon, both. Although if it can really said to have been Horus who was there for the genesis of the latter, I am not so sure. Horus Lupacau, that first found son, took a ganger from the minds of Cthonia and made him a warrior peerless, a captain amongst captains, a conductor in the symphony of galactic violence that was the great crusade your emperor threw into the stars. But the father who forged Ezekiel into a legend amongst legends was not he who emerged from the serpent lodge on Daven. Horus the Warmaster, the vessel of the Pantheon, was a thing so swollen of power etheric that he became a dark fulcrum around which space and time turn. He was not a man at the end, he barely even resembled one. He was akin to a void in the rough shape of something once human, a fount of unimaginable energies that to look on him brought one pain on levels physical and metaphysical. And by then there was not a single ideal or principle that he would not cast aside in order to sit upon his father's throne for but a fleeting moment. Horus lost his war because he lost himself, and it is that failure that destroyed his first captain, his son. By the time the emperor slew his misbegotten welp on the bridge of the vengeful spirit, the work of forging the true Ezekiel Aperdon was already done. If he was as disgusted as you say by his father's enthrallment to the Pantheon, then why would he seek their patronage? You are mistaken. It is the gods who seek Aperdon's patronage. They whisper to him constantly like they do no other. I'm not even sure if those baseborn sons of your wretchling corpse deity hear their voices so keenly. Imagine how that must rankle them. Or at least some of them. I doubt the Lord of Iron cares, but that golden priest on his planet of churches, oh how it must burn to know the mongrel child of his brother's barbarian rabble, converses with the fundamental finalities of the universe in ways he can only dream of. Although, converse is perhaps the wrong word, it is fundamentally a one-sided affair. They speak, he listens, when he cares too, and picks from their susurrations whatever he wants or needs. For the most part, I believe he ignores them. What sets them apart from his father is that he is never so foolish as to trust them. It is always a choice when it comes to the Pantheon. Surely you must be aware of this at this point. The choice is the only thing that matters. If you elect, take whatever the gods offer, even if it is only words, you must be aware that even this will come with a price. Ezekiel understands this better, I believe, than anyone, and he has chosen freedom. You speak as if he doesn't even care for the gods he serves. My dear chronicler, he despises the gods. How can that be? How could it not? How can you possess even a modicum of self-awareness in this universe of ours, and not hate the gods? The Pantheon. All gods. Divinity is some horrid expressance of this universal system. The divine are nothing short of monstrosities. That is... You do not even bother finishing that sentence. The being you pray to is a barely functioning collection of necrotic cells that no longer even bears the shape of the man he had the ego to claim he was. He lies here, squatting at the center of this world and glugging himself upon the pitiful masses that grow themselves into his maw, with the tears of devotion still streaming from their burnt-out eyes as he devours their very essence. This is not to say that the Emperor and the Pantheon are as bad as each other, that would be insulting to your intelligence and mine, but they are all, to one degree or another, soul-vampires, suckling at the neck of reality. You and I are both men of learning, of science in our own ways. You would have been quite at home in Tisca, I expect, had you been born 10,000 years ago. You seem quite dedicated to applying concepts unheard of in this era. Inquiry, empiricism, rationality. Well, we must acknowledge divinities exist beyond that, frustrating as it may be. By some quirk of however this universe functions, things that should remain in the realm fictitious have an annoying habit of becoming all too terribly real, lurking in the warp behind the veil of the actual. Simply through the act of belief, through abstractions abominable coalesce there, and as the veil wears thin, so too do our desires gain horrific actuality. We give them all they need, it is seemingly impossible not to. Your Emperor tried, you know. Ten millennia ago, his secular imperial truth attempted to starve the pantheon of their fuel. I'd call the attempt noble if it were not so utterly, breathtakingly, impossible. But that was the Emperor, a being so egotistical he believed he could break the universe itself over his gold-armoured knee. I cannot quite say I expected any of that from one such as you. We are not all the frothing cultists your lot are always so keen to portray us as.  A lot of us think, you know.  You think this does not fuel them? Oh it almost certainly does. But such is the nature of deals with devils. Power is the only thing that will keep one from their grasp, and yet it is the very thing they trade and treat him. In their most magnanimous curses is to be found enlightenment and potency, and it is by these strings do we dance to their tune. They need us to simply exist, yes, but we so often find ourselves in need of them. Abaddon knows this. If anything he was the one who enlightened the Ezekarian to this truth. It is a spectrum. You may have Ezekiel on one end, the lunatics of the 17th Legion on the other, and the rest of us adrift in between, charting our own courses in a universe so hilariously, fundamentally broken. You think our goal is petty and mortal? My friend, to be so hopelessly mortal is the only path one can justifiably take, when wherever one turns stands a carrion entity eager beyond words to glut itself upon your life and soul.  Petty doesn't even begin! You perhaps underestimate even an astartist capacity for the barest of mortal passions. The satisfaction of achieving what we may is one of the simplest pleasures the curse of existence grants us, but we achieve it in spite of the gods. Spite, oh yes, for they are spite incarnated. They raise us up, swell us to the brim with their beneficence, only to cast us low on literal whims, dash us upon the rocks of fate simply because it would make for a better story. Remember earlier when I said Ezekiel ignores them? They hate that. In as much as an intelligence made purely of malice and ill will has the capacity to, they loathe him for his resolve, his capabilities, and so will seek to deny him when they can. Yet they need him as much as he needs them. Do you see? Are you beginning to comprehend? I have related this to my inquisitorial handlers before, the oraborical nature of Abaddon's destiny. He will never kneel, so perhaps they will never let him succeed. The Pantheon betray their chosen? I, for one, believe so. Never underestimate their capacity for vindictiveness.  It is fascinating, a relationship built on as much mutual need as it is on mutual apporance. They each work to outmaneuver each other, to attempt to gain more out of their bargain than the other party, and in doing so work as against each other as they do in parity. It is a great game played across the lives and souls of billions. I wonder off it if it can ever be won. I doubt it, but perhaps the satisfaction lies not within the ultimate victory, but in whatever vindication you may glean along the way. Violence appears where power is in jeopardy, but left to its own course it ends in power's disappearance. It is not correct to think of non-violence as the opposite of violence. Violence can destroy power. It is utterly incapable of creating it. One of your late night musings? From one of the last European philosophers of late M2. I'd have thought a scholar of your ability and experience would be familiar. I do believe I'm beginning to like you, his daughter. Answer me this, Kaon. If all you say of the Dispoiler is true, then what is the point of it all? Are the ten thousand years of horror and atrocity you have inflicted upon us, upon the Imperium, the result of not but greed and wounded pride? We are merely fulfilling our purpose. I have no patience for any further riddles. I would have thought it obvious to an apparent man of learning, such as yourself. The legionary's astarties were created to be the breakers of tyrants, forged as living weapons to lay low those who would deny mankind its rightful mastery of the galaxy, be they Xenos squatters or petty human potentates. So, what? You are the stalwart defenders of the species? Give me, but even by your standards, that's laughable. Oh, I will not deny that this is personal to us. It absolutely is. The Imperium was forged with our blood and blades, and we intend to take it back from the fools who thought we would meekly follow the Thunder legions into oblivion. We were tools of the Emperor to be cast aside once our purpose had been fulfilled. What uses a weapon after the war has been won? Well, we are what happens when the weapon realises it's a weapon. We denied our fates. We were not slaves to some attached, psychorabomination's ego. No, you merely became slaves to tumourous horror things from beyond the known. You really don't see, do you? Even now, you remain blind to it all. We are all slaves to these things that suckle on our devotions. We, the Legion, we are the ones who deny this. I am sure plenty of my brothers settle upon service to the Pantheon as the ultimate truth, be it in the form of actual devotion or simple nihilistic surrender. But remember, your Emperor made us this way. We are who we were created to be. We are the vengeance of the Spernd, the rage of the discarded and betrayed. The universe is broken, Historiter. We may as well get some recompense for that, before our souls are eaten piece by piece, as every iota of the fire that we once were screams in an agony beyond description. Touched and nerve, have I? I know what you are, Khaon. Oh, do tell. You're not a herald or a spy.   My high charms work, and these mine enemies are all knit up. In their distractions, they now are in my power. Throne, I had to see it to believe it. Restrain that man! Inquisitor Kalendra, I- Silence! So much as speak another word, and even the Lord Regent himself won't be able to save you. For now, thank the Emperor that you're leaving this room alive. Oh, delightful. Forgive me, Oculus. You have far more nerve than I gave you credit for. Is that you, Verity? It has been a while. Hold your tongue, heretic. Before I have it torn from your skull and nailed to my desk. As for you, Oculus, we will continue this conversation elsewhere. Inquisitor, I have every right to- Armsman, I said, restrain him! Do come again, my friend. The Historic's work is never done. Head on over to patreon.com. If you'd like to receive more updates about the channel and any future videos, you can contact me or follow me on Twitter at OculusImperia. Otherwise, please like, subscribe, comment, let me know your feedback, and as ever, thank you very much for watching.


Staying safe while getting better, protecting yourself and your loved ones while taking radioactive drugs. You are about to be treated with a radiopharmaceutical, a drug that contains radioactive material. You are not alone. Each year radioactive drugs are administered more than 16 million times in the United States to diagnose and treat a wide variety of diseases, including cancer. The Nuclear Regulatory Commission, along with the Food and Drug Administration, develops regulations to ensure these drugs are used safely. You have an important role to play. Before you begin treatment, talk to your health care team so that you understand your treatment and what you should do before, during and after treatment to make this experience safer for you and those around you. These drugs can help you, but they may also make you radioactive for several days or weeks. This does not mean your family, pets and friends will be in danger. There are some simple steps you can take to limit their exposure to radiation. Your health care team will give you specific instructions to follow for a set period of time after your treatment to keep radiation exposure to others as low as possible. Your doctor will consider your personal living situation and your instructions may be tailored specifically for you. Make sure you understand and can follow these instructions. Your doctor's instructions will likely follow three basic principles – distance, time and hygiene. Distance – the farther away you are from others, the less radiation they will receive. You may be advised to avoid public transportation such as buses, trains or airplanes. Avoid close physical contact with your family and pets, including hugging and kissing. Time – the longer people are near you, the more radiation they receive. You may be advised to sleep alone for a period of time or limit long car rides with others. Hygiene – washing your hands often and showering daily reduces the amount of radiation you leave on surfaces around you. You may be advised to use a separate bathroom. Do not cook or prepare food for others. Use a separate set of dishes and silverware and don't share them. Wash your clothes separately from others and separate your trash from the rest of the households. Some of these best practices of radiation protection mean you may be separated from loved ones when you most need their emotional support. But remember, these steps are important to protect the people around you, especially young children. And these steps are temporary. Radioactive drugs can be used effectively and safely. The best way to protect your family and others is to follow your health care team's instructions carefully and consistently. Presented by the United States Nuclear Regulatory Commission. For more information, visit the website www.nrc.gov.


Live from London, England, it's theCUBE covering .NEXT Conference Europe 2018. Brought to you by Nutanix. Welcome back, I'm Stu Miniman. You're watching theCUBE's coverage of Nutanix.NEXT 2018 in London, England. You Piscar is my co-host for two days of live coverage and happy to welcome back to the program Dan McConnell who's the vice president of Open HCI Solutions at Dell EMC. Dan, great to see you again. Great to be back. Always, always a pleasure to see you guys. Yeah, so Dan, actually the first time I met you, the same time I first met you was at the Vienna show two years ago. Dell has multi-year partnership with Nutanix. I see a state side here and there at some Dell events also. But Dell is Nutanix, biggest partner as an OEM, so give us the state of the state. Just actually just crossed, I think last quarter crossed over a billion dollars in sales. And I think overall, I think Deirdre said that three billion dollars worth of total sales, so a billion of that through Dell, that's pretty impressive.  So it's a strong business, continues to grow, over 2,000 customers and growing, continues to be a good partnership. It's funny, you say, but it's not joking, it is every quarter is the biggest quarter ever, despite some people in the marketplace, oh well, that relationship's dead, right? So talk to us a little bit about the portfolio and where it fits, because obviously the revenue numbers speak for themselves, the customers and the like, so. Absolutely, yeah, I guess my easiest way to describe it is HCI is, it's a style of architecture, right? Just like in the old three-tier land, we had multiple arrays and our servers support multiple operating systems, just like in HCI, we're going to address the market with a portfolio. We've got more than one product, it is a portfolio to address the whole market. So, you know, the partnership continues, strong focus on enabling the Nutanix stack, you know, we're going to be the best hardware infrastructure solution for the Nutanix stack. It's a portfolio approach, it's what makes us the leader in HCI across all of our products, and it will continue forward. So, I know we always get that we've got multiple HCI products, which one will win, HCI is a style of architecture. We're going to have a portfolio. Yeah, we saw that in the CI space, we see it in every market, there's, you know, especially storage, there's never been somebody that tend to get more than 20 or 30% of the market. So, what makes it special? You know, running Nutanix on Dell, what makes it special? Why would customers want to do that? Let's see, how long do we have? Easiest description here, one, I'll focus on LCM, life cycle management, the thing that is core to Nutanix, ease of use, one click, I'll use the one click. We actually have the best integration into their overall LCM package. It literally is, one click, from the Prism UI to update all of the server firmware as well, and it's cluster aware. So, we know how to evacuate, we know how to flash stuff, bring it and repatriate the data. So, it's actually, it's something we have that no one else has. We are, once again, like I said before, we will be the best hardware platform, best infrastructure to fit in to the Nutanix stack. I'll say stack these days, because what was once HCI is now a list of single word products that is a pretty big stack and growing. So, zooming into the LCM bit a little bit, so back when I was at a customer, I built IS platforms, and that whole operational pain of keeping hardware up to date, keeping the firmwares up to date. That sounds like a very operational benefit for the teams working with those products. So, how does this work into freeing up time for people doing other stuff on that platform? That is what I would say, that is the whole goal. I hate to fall into catchphrases, but invisible infrastructure. The goal is to make the entire infrastructure easy to use, simple, invisible, such that more and more, IT is becoming the differentiation piece for businesses, focusing much more up stack in how to innovate around the business logic side of the equation, as opposed to the infrastructure componentry. So, as IT becomes an innovation engine for customers, they need that focus more towards the business, less towards the bits and bytes of the infrastructure. All of what we do is focus to enable that. So, even if you're talking about making infrastructure invisible, making it easy to operate and manage, there's still a lot going on in the infrastructure space, specifically around hardware, and around running applications. So, one of the things I learned is that you are now certified to run SAP on top of it. So, please explain how that works, what it means for customers. Absolutely, absolutely. I don't know, we sat in places like this four years ago and people would ask, well, what workloads are good for HCI, and we'd say, you know, test Dev and VDI, and nowadays it's everywhere. You know, it's what workloads aren't good for HCI, and I would have told you two years ago, well, big databases, blah, blah, blah, but at this point, everything's within HCI. HCI has been the style of infrastructure that can run any workload, and our certification with SAP is just a huge instantiation with that. Matter of fact, I believe we are the only one that has a two socket and a four socket certified from an HCI perspective. Something we're very proud of, something we'll continue to lean into moving forward. Yeah, yeah, Dan, absolutely. When I've been talking to customers, it's those edge cases. It was, you know, my AS 400, I haven't migrated over because I'm actually going to modernize and put that stuff in the cloud or things like that. So, any application, you know, on HCI today, Nutanix hasn't been sitting still though. We've been listening for the last two days, really growing the portfolio. What is the increase in products? What does that mean to the offerings from Dell? Sure, obviously, we'll keep up with the Intel heartbeat, you know, things like, we have a pretty big refresh coming up here early next year with what Intel calls Cascade Lake, and also not just keeping up with the generations, but as Nutanix evolves, things like buckets, you probably heard buckets mentioned this week. We've got some hardware platforms. I won't pre-announce any of my PowerEdge brethren platforms, but we have some hardware platforms that are focused much more around storage density. So, obviously, a great fit for something like buckets. So, we'll look at, as Nutanix matures, what is the best fit hardware for that stack? Yeah, and maybe there's been some updates as to how you handle the hardware and software working with Nutanix. Maybe explain how that works now. Absolutely. So, we've got, you know, we call it XC Series and XC Core. Series is the appliance. It continues on just as it was. It is, you know, software licensed and packaged with the hardware, and then recently we announced something called XC Core, which has all of the goodness of the pre-installation, all the software comes on it, it's easy to use, but the licensing, you can bring your own licensing, so to speak, from Nutanix. So, the flexibility to either get a licensing with the hardware in an appliance form or bring your own license in XC Core, as is what we call it. Yeah, that's really interesting. When I think back a year ago was when Nutanix started talking about moving to all software. And, you know, one of the things I said is like, hey, this is a great opportunity for Dell and Lenovo just to pull everyone in, but it's not that simple, right? Some are still going to want to buy the software from Nutanix. Have you seen much change in your business and interactions with customers in the field after that change, or how's it gone? The short answer is no. Yeah. And before the formal introduction of Core, we were, you know, we had, we called them ELAs. Yeah. You know, we had large customers that were buying things in different forms, which is why we went to go do Core. We saw this trend happening. And with their shift to software, it just made sense. So, you know, the engagements with customers can continue to be, I'd say, almost in an appliance form, even though they're buying the software directly through Nutanix. So it's a similar engagement, similar go-to-market, different fulfillment model. Dan, how about looking forward? What do you see for the portfolio overall and any places specifically we should be watching? You mentioned buckets. Anything else from the Nutanix side? Sure, sure. I, you know, I obviously continue the LCM focus. Well, one, I'll start with SAP. Continue focus. We're going to lean into SAP. We see that, and broader, what I would call enterprise workloads. But also, the Intel refresh coming up from a hardware perspective, you'll see Cascade Lake roll out across all the platforms. Also, you'll see us potentially introduce some new denser storage platforms for things like buckets. So, continuing to drive the portfolio forward, offering it both in an appliance perspective as well as a core perspective. So it's a really broad portfolio today and you'll see that continuing forward. Yeah, just to actually see, you mentioned that there was a big focus talking about, you know, the new memory type of options, new things like NVMe, you know, I know I've heard lots from Dell as to where they're going. So potentially that's areas where I would see that and some of the cloud native applications and the like, see the natural fit for Nutanix plus Dell in the future. Sure, absolutely. You'll see, you know, incorporation of NVMe, some of the storage class memory stuff. So without pre-announcing, so to speak, I obviously Dell from a hardware perspective keen focus on these upcoming technologies and how to enable them. We work tightly with Nutanix on how to integrate them into the stack. So you will definitely see us take advantage of things like NVMe, things like storage class memory. It, you know, it's, technology never sits still, right? And we're going to, you know, the partnership we have between Nutanix and Dell helps us keep on the edge of that. Sorry, I'm going to use the edge for it. All right, if we had another hour, Dan, we would go there and talk about where that fits. But I think we do have to leave it there. Thanks so much, Dan, for the updates. As always, congratulations on the progress and look forward to hearing the best quarter ever, you know, in the future. And thanks so much for watching theCUBE. We'll be back with lots more coverage here from Nutanix 2018 EU in London, England. Thanks for watching theCUBE. Hi, I'm Stu Miniman.