And welcome back everybody. This is the Q, SiliconANGLE's premiere video production, where we go out to the top tech events to bring you the latest news on trends in enterprise IT. We're here of course at .conf 2012 Splunk's annual user conference in Las Vegas, we're at the Cosmopolitan Hotel. My name is Jeff Kelly with wikibon.org, and I'm joined with my co-host Jeff Frick from SiliconANGLE. Thank you Jeff. Welcome back everyone. We are really excited to have Gido Schroeder with us, the VP of products for Splunk. He's been a really busy guy here not only the last few days at the conference, but also obviously getting these products up to snuff and getting ready for the show. Gido, welcome to the Q. He tells us he was out early this morning hiking with the team, doing some good team building. Good morning Jeff. We're glad you made it back. Good morning Jeff. No rattlesnake bites or any other mudslides. No, we are doing well so no harm. That's one of the great things at Splunk, this team building. We want to do something together and one of the things I really enjoy being with this group. Yeah, it's interesting you bring that up because we looked up your LinkedIn page, you were at SAP for a long time, 10 years plus and now you're at a new place, a new company, a new industry space. So how's that been? It must be exciting. Yeah, absolutely. I've been in the US now for 12 years. I actually came to Palo Alto, end of 99, was supposed to be initially just for one year as a kind of fellowship with SAP, and then we appended a year and appended a year. I was often thinking maybe join a typical Silicon Valley startup type of company and I passed on a couple of opportunities and I've been for 15 years at SAP and I thought maybe it's almost too late to get anywhere else. So when I got to know then Godfrey and Eric earlier this year, it took me a little while to get my arms around what Splunk is really doing and completely understand the opportunity, but once I saw it, I thought this is really something I want to do and then I made a decision and I don't regret it. So I'm with the company now for half a year and it has been a blast. It's a great team. I enjoy Godfrey's leadership and the rest of the management team. It's a fantastic crew and also the team that Eric inherited me. So you saw yesterday during the keynote that we did a kind of handover of the torch from Eric to myself and I appreciate the trust that they are giving me and you've heard what we are up to. We want to grow the company quite a bit and it's going very well. I mean in the moment, the company is growing 60, 70 percent revenue year over year and I think they wanted to have a guy who has seen a large company before and operated at a larger scale and that's what I did at SAP. I had a global organization distributed over seven countries, more than 800 people and I think that's certainly a valuable background and experience that can I bring now to Splunk. Interesting, so I'm eager to get your take as a newcomer to Splunk and having spent a long time at SAP and kind of in that enterprise software world, how do you see Splunk, is Splunk disrupting and not just Splunk, but big data in general Splunk specifically, is it disrupting the traditional business intelligence data warehouse industry and so how? Because it seems like it is certainly big data is a different approach, it's more flexible, it's more extensible versus a little more structured world of the enterprise data warehouse we're used to and coming from SAP with their business optics portfolio, I thought you might have an interesting perspective. Yeah, you know it's kind of interesting. I mean I didn't stumble across Splunk in my past life at all and if you study the BI quadrants or forest highways, I don't think you would see Splunk showing up in any of the BI coverage. I don't think the market sees us really in that space right now. We are seen in some other areas like enterprise security for example, and IT operations, application management and Splunk is certainly very different. I mean one of the things I found very differentiating from what most of the BI companies are doing is that we don't have this notion of a big modeling effort when you start to use a tool. I mean most of the BI companies they're required to define some sort of schema, a cube and structure of the data and that is a notion I think that Splunk really doesn't have. I mean typically what we do when we come into a customer we identify what data is interesting for the customer, we suck it into our system and then you can answer pretty much whatever question you have after it but it doesn't require this big modeling effort. So that's one big differentiating area. The other I would say key difference is the viral nature of the product and that's something I you know probably a lot of the big guys like SAP and IBM with Cognos and whoever has a large BI portfolio would like to get to is a small product that is easy to download, easy to implement and with a very small footprint and I think this is really something that Splunk got absolutely right. So if you go to our website and that's where most of the customers actually start, right? They download our free product and start in a small department with a project and then at some point in time they understand it and see other use cases and grow it and I think that is really relatively easy possible with Splunk but not with many of the other enterprise products that you find out in the market. Yeah it seems to be an increasingly successful way to go to market to have that way for customers to do trial. We did some work with Atlassian in the past and they have a similar thing where you can it's not free it's $10 that goes to charity which is a brilliant twist on the theme but you get in you try it people learn it and they do get this viral nature as more and more people get exposed to the application use the application and it's actually something that the founders did very deliberately so I sat down with Rob Das and Eric some time ago and they told me all the whole story say how they started and I mean it's kind of interesting what they did I mean first of all they really took some time off to understand what is an interesting problem to solve I mean they had a couple of startups that they did before which didn't go that well and then I think they sit down you know what we really want to go after something that you could make money with yeah and that solves a really tangible problem and I think they spent a couple of months to just interview companies and then they heard hey wow yeah there's there's really one you know kind of reoccurring recurring pattern once they spoke to all these different companies that they have really a hard time to understand you know what is the information that is hidden in those logs and other data sources that companies have in their IT infrastructure and it just turned out to be damned hard for those companies to get really to that information yeah that was one thing and the other thing I believe they realized is that they wanted to change the way how this stuff is sold yeah I think they had an experience how enterprise software is sold yeah it was a long sales cycle and a sales executive yeah that needs to build relationships and whatever right and I think they wanted to do it just in a different way you know let let's a customer or people at the customer do the selling themselves yeah and that's how it typically works today so a customer starts they download the product get started with free and then they scale up at some point in time and they actually do the selling for us yeah which I think makes it a lot easier yeah yeah we had the target guys on the cube earlier and that's exactly what happened with them one of them had a little project downloaded Splunk solve the solve the project and what they said they've only been using Splunk since November it's already taken off and they've got initiative after initiative after initiative planned out that they want to they want to come up with the product and you know I think one of the other things yeah that that probably Rob and Eric and the early crew got right it's a we're looking from the very beginning at very concrete use cases yeah and we're very close to the customer and for my past experience I would say that that is really the way how you can innovate the best sit down with a customer understand the problem and then co-innovate with the customers and I think that that's really what the company did for a long time and we try to continue to do every year on the customer understand the specific problem and a lot of the things that you see now coming also into course Plunk in the core product I think are all driven really by what our customers are telling us so I'm interested to hear about how from a product perspective yeah you know the idea that Splunk often starts off in the in the data center and then moves to other use cases in particular customer circumstances what kind of pressure does that put on the product team to to keep the product flexible enough to expand to use cases that you've never really thought of before and that your customers are coming up with you know I mean I spoke also yesterday in the keynote about it I think was Splunk growing up now I think we become a lot more aware and this is really happening when you look at how our customers are using the product I think we become more and more really a platform yeah that addresses a broad range of use cases and yesterday in the keynote I showed a little diagram I mean either you call it virtual circle of platform or fly wheel and you know I think there are a couple of principles that we need to go after and look at to make this a solid foundation and a viable platform yeah so I spoke about reuse and that's what we are doing now for a while that we take the same architecture the same stack and use it across this large spectrum of different use cases yeah whether it's application management IT operations enterprise security PCI compliance web analytics and what have you and I think this reuse principle allows us really I would say to focus on making this optimizing this platform yeah optimizing the elements of the platform the index the search the visualization and I think this wouldn't happen if we would build off build one of type of solutions and applications all the time so I think that is very important then I think we learned a lesson about the product needs to be more modular yeah because we can do everything customers find you know different things that they want to do with it and and that's where we started now to open it up right yeah so we make the product more modular and one big push I mean we are talking a lot about this during the conference is our developer interfaces yeah that we have added to it and one one of the things that I liked very much about Splunk from a I would say architectural perspective yeah is that they kept it very clean the design in terms of having a rest API between the core engine and the consumption so all the UIs that are built on the engine are sitting on this rest API and what we have done now with the the SDKs that we kind of just put a language specific wrapper on top of the rest API that was already there yeah and so we have now a number of SDKs available for Java JavaScript Python there is one in PHP that is in the makings and we will add to that yeah to just make it very easy to consume for developers in whatever programming language environment they are sitting that's another example yeah of I would say how this experience of growing now growing up you know drives I would say new things that we are doing in the architecture open it up for consumption by developers making it more modular we are also going after more I would say interoperability and integration with the different applications because I mean if you look at what our customers are doing you know when they start to get bigger these things don't exist in a in a silo yeah they don't do just you know IT operations but it's all connected yeah they need to look for the infrastructure they need to look for the applications they also need to go after enterprise security and you have seen yesterday in the keynote some examples yeah Will Hayes spoke about it how these things are kind of all connected together and I think now as a platform yeah we can do this right and that's really exciting yeah too I would say makes us much much bigger and get a lot more impact really interesting story about you know having the real discrete use cases and having a really easy way to try and buy and use but at the same time architecting for a platform play for down the road when the big you know as Dan said the data fabric trying to be the data fabric for the enterprise and being able to parallel track those and then of course just it seems to be the what everyone wants to do now is then to extend your capability through a community with the partner apps and enable them to extend into ways that you guys and your team haven't even thought of or you know very specialized use case or whatever you know I mean one interesting thing and you can hear Godfrey Salew and our CEO talk a lot about that maybe the biggest problem we have at Splunk right now is to decide what we are not doing yeah because there are so many ideas and customers come come to us and I think that that's also part of my role now to do a proper prioritization exercise with the team and say okay with our capabilities that we have right now these are the things that we are going after for the next year it's a pretty agile shop I mean this is also something I like I mean we are not making you know grand two years three-year plans sure we have a strategy and also a long-term strategy but we execute in a fairly agile way so pick the top things from the backlog and then execute very well and I mean this is also impressive you know how the team is actually executing so it's a very well run shop you know that produces a lot of I would say rock solid software every every year and you have seen the pipeline here it's a conference right I mean for for the size of the company and and the engineering organization you know I'm proud and I was also impressed when I came in about the magnitude here of what the team is doing so enterprise scale big data story we do a lot of visualization we have this broad spectrum of applications here which is really rich in a lot of different areas that require also quite a bit of domain skill and then we you know just launched two weeks ago our cloud-based product storm right so there's a lot of stuff going on for for the size of the company I think we need to make sure that we are not trying to swallow too much right well you spoke of priorities so maybe you could curate clue audience into what those priorities are for you for the next six 12 months and what are the real I guess what are the real challenges for Splunk over the next year and where the priorities you're focused on so what's going on right now I would say are two big things number one is continuing our scalability and performance story I mean you heard a couple of announcements here during the conference so index clustering which I think gets us really to the next level in terms of resiliency and then we have also you know some of these acceleration features report acceleration we spoke about and that story will continue we have a couple of I would say follow-ons to these stories in the next release which will come next year so you know there is a tradition that Splunk to name our releases after ponies this one we just chipped was the ace release I don't know if you have ever heard the story but no what is the story of the pony I think it's worth because I keep saying ponies all over the place there were ponies at the party last night there was like virtual ponies in 2012 teleporting from I don't know where they were teleporting from it became the mascot and you know I'm not the historian of the company and I'm not sure if I tell it completely correctly but as far as I recall you know Eric wanted to do and and he has a very generous soul and always cares about people yeah he wanted to do something good and ask one of the developers hey what can I do for you I think the crew has worked very hard and he said you know can you get me a something pony yeah and a week later he actually brought a big mascot pony into the office and it kind of became the mascot of the of the company and you know now the releases are named after after these ponies I think it must be something for children attire so there there is ace the next one is be what is it bubbles bubbles the next one is bubbles and after that comes cupcake well it's what's funny I think actually it was a lot it was a much well thought out plan to get you to join them because if anyone's ever been sap in Palo Alto there's more ponies and horses around that office building that I think there are anywhere else in the peninsula off of page mill road but it's been great we're here with Gita Schroeder the senior vice president of products force Blanc thank you really a lot for your insights it's it's it's really fun to hear your enthusiasm having been in a big mature ERP company and to come into a startup environment be so enthused and be you know but still back in the enterprise changing the world and changing the way a lot of people are doing their business oh I don't regret the move yeah and Splunk is hiring so if anybody else wants to join us we will probably hire into engineering next year about 50 people you know in the Bay Area and I can tell you it's a it's a fun place it's interesting stuff we are working on yeah and a fantastic team to join so if you're ever contemplating you know also making a move checkouts Blanc I think it's a it's an excellent place thanks well he's not enthusiastic is he I mean come on so here we are we're gonna sign off now we've got another guest lined up we back in just a few minutes we're again we're at the Splunk conf 2012 in beautiful Las Vegas the cosmopolitan hotel we'll see you in just a few minutes


Alright, so in this video I want to talk about the two skills that I believe everyone should learn if they want to make more money and This applies to if you're a business owner if you're building a personal brand You're an influencer or even if you're just like working in a nine to five and just a normal corporate job These two skills you're still gonna want to hone and improve these two skills So we'll get into those skills in just a second But before I do if this is your second or more video on my channel that you're watching I have something called the gentlemen's agreement which means if you've watched two or more videos and you're finding it valuable All I ask is that you hit the like and subscribe button It is free for you to watch these videos But it takes a lot of time and energy for me to come up with the ideas and shoot these and publish these on a regular Basis, so that's all I ask just if you're enjoying it, please hit the subscribe button It would really help me out. So let's talk about these two skills What are they and why should you learn them if you want to make more money? These are two skills that I've learned and improved over the last six to seven years But even more so in the last three years. So three years. I've been building my personal brand I started in January 2020 so a little over three years But these two skills have really helped me to do that and it's actually the reason why I started my personal brand Was to get better at these two skills so What is the first skill number one is writing and I've done all kinds of writing and I still do all kinds of writing. I write every day I write just my perspectives and opinions and insights I share those things in my email newsletter and on social media channels like Twitter and Facebook and LinkedIn and Then I also do persuasive or conversion writing or otherwise known as copywriting Which is salesmanship in print right you're writing to get someone to take a certain action so I Think writing no matter what your career is even if you are filming YouTube videos as an example as your career It all starts with writing it starts with writing a script writing down your ideas writing down the flow or the outline of what you want To cover in your video so almost every career you are pretty much every career everything you do even if you're not working You're gonna be using writing. It's how you communicate So I believe everyone should improve their writing skill They're writing ability and the way that you do that is you just write a lot you study good writers You stutter writing that makes you think it makes you see something in a different light and makes you take a certain action So an example for me is I have a swipe file of my own and whenever I buy something I look at the ad I look at what was written in the ad to get my attention And I put it into a little folder on my Google Drive account And so when you study good writing and then you also write every day, which I write multiple times per day You get better at writing magical. So I've been doing this I've been writing every day to my email list since January 2020 So I've written over 2000 original emails And so the way that I got good at writing is just to write a lot study good writers and write Regularly write consistently. So that's the first skill is writing and the second skill is speaking So right now I'm speaking to you in this video But speaking is another way of communicating, right? So if you can command an audience, let's say you're a public speaker Right public speakers make boo boo bucks if they are someone like Tony Robbins Who's a motivational speaker and he sells programs and he combines it with his writing? So I think speaking it's one of those things I come from Hawaii And it's one of those things where everyone's afraid of doing right public speaking or improving their speaking ability Which means it's an advantage for everyone who learns the skill So speaking is one of those things that it made me very uncomfortable when I first started shooting videos and learning how to speak better But the reason why I created my YouTube channel was because I wanted to improve my skill of speaking I want to become better at it because I knew how valuable it was if you can speak confidently and persuasively explain your ideas But I knew that it would help me in every area of my life It's made me a better communicator not just in business, but in my relationships with my wife with my family my friends And so speaking is another one of those skills no matter what career what role that you're in or even if you don't work a job It's going to help you it's gonna help you make more money because you come off as more confident You come off as smarter you become or you you come off as more successful because you're able to explain your ideas And you're able to be more persuasive So a speaking is one of those things again, not many people like to do it because it's very uncomfortable It's especially if you're speaking to a group But if you learn that skill speaking you combine it with writing I can almost guarantee you no matter what your career is or what you do as a profession You're going to make more money You're going to be using those two skills writing and speaking the way that you learn is you first study it study Good speakers study good writers and then practice doing it find a way to practice doing it Ideally every day, but if not every day try to do it as much as you can and Social media is great. You can publish things regularly publish your thoughts You can publish writing pieces You can publish your videos on a platform like youtube or tiktok and it all starts with just Getting that first step getting that first video or first writing piece out there So those are the two skills I believe everyone everyone should learn if they want to make more money Whether it's a business personal brand or a nine to five or just a regular corporate job So hope this is useful if it was give me a like and let me know what you thought about this video in the comments And i'll see you in the next one. Take it easy


The study aimed to compare the predictive accuracy of a set of recognized and novel features extracted from smartphone-collected data for detecting generalized anxiety disorder, GAD, social anxiety disorder, SAD, and depression in a non-clinical population of 112 Canadian adults. The results showed that models of SAD and depression achieved significantly greater screening accuracy than uninformative models, while models of GAD failed to be predictive. The study suggests that smartphone-collected data can act as broad indicators of mental health and can be used to study, assess, and track psychopathology across multiple disorders and diagnostic boundaries. This article was authored by Daniel D. Mateo, Catherine Fotinos, Sikinthia Locuge, and others.


Hey guys, welcome to another snow break video and it's about time that we do a little bit of a preview of Katya's skills. This information I had to translate from Billy Billy so that we could have an advanced look at her skills and a rough translation of the text of her skills. So let's move on to what she has for her skills. So we have here, these are just gifts of GIFs of her skills so these are not videos so these repeat over and over. This is the first part and this is the second part mostly in shows to her skills. We'll go straight to this one. So this is going to be her standard skill. So basically her standard skill is actually nice to look at but again we'll have to look at the translation. The translation is actually this one from this one that I made here. So let's take a look at the translation here. This is her standard skill so travel through Hades so it's not even Katya's Kasia client. So switches to third guide form. Third guide form is basically her burst form. She cannot move but will exit if you do a dodge. So in this form as well you can restore regular energy and each shot will consume additional regular energy and after the regular energy is exhausted she will exit the third guide form as well. So the form has three exits actually not two so the first one will be a dodge. The second one is when she exhausts all the energy with that burst damage for the standard skill and the third one is let's just go through this. Her resistance will be improved, hopefully it will be better than Fisha Hush's ultimate so during this period the final damage caused by shooting item is corrupted power and burst energy obtained are reduced. This pertains to the interruption of the damage she's going to be receiving. Then in this form Katya obtains different firing speeds according to the crossbow mode I don't know what this is yet. This is still up for us to know when the game launches with her and using walk through Hades so travelling through Hades. If you use the skill again you can end the first one. It's kind of confusing actually. So after exiting third guidance form walking through Hades begins to calculate the next interval in which this refers to the skill cooldown again refresh. So this is again guys her burst skill for her standard skill three exits for that so dodging then exhausting of energy and casting the skill again. Okay for the support skill so we have here it's actually very obvious so she actually does freeze a couple of enemies right in front of Absconditis Haru and she makes sure that they don't obviously move and it's going to be I think on a limited area. As you can see there's a cone area in which she shoots that as a support skill. So let's go to the actual text for that one so it's called Explosive Messenger. Contact line is forward winter wind arrow towards the target in the field vision so it's going to be a cone target after the explosion winter wind arrow cause a low temperature damage and applies a freezing effect so freeze damage freezing effect I'm not sure yet on what the duration is but this is actually very basic. So she is also going to be kind of a good support kind of the opposite for life at this point giving you also a freeze support skill. The last one is going to be her ultimate skill so as you can see there as she fires a lot of arrows with that animation it tracks the target wherever it goes so it has a wide range area I think the arrows give it a bit of a slow because of the freeze damage. Let's go through the actual text for that skill here so we have eloquent speech shoots it to the sky dropping deception arrow rain with a tracking effect on the target so as you can see there it was being followed causing low temperature damage once every certain time and deception arrow can last for a certain period of time so there's no slow in the text but it tracks the enemy where it goes and lastly is going to be her passive skill okay inheritance of deception each shot has a certain probability of obtaining a powering effect that will shoot an additional low temperature damage of a certain percentage to the top power so this is just additional frost damage if I may say for every 10 alignment index the damage multiplier will be additionally increased so this is actually very simple straightforward and I hope the damage is going to be significant for this one so her skill is her kit is basically very straightforward so she has freeze she has she has she has frost sorry she has freeze damage she does have burst damage and I think for her skill she is going to be used more for single targets bosses perhaps she'll be very very good so hopefully that is actually going to be accurate because if it's that we're not sure yet on what the crossbow mechanic looks like on the regular attack damage so these are just her skills including her passive skill so that we will have to look at because the crossbow weapon type is actually new we'll have to take a look at that and explore that as the update arrives so what do I think of her skills I think it's worth a look I'm actually curious on where we can bring her in certain stages of the game will she be a redundant type of unit because we do have single we do have ya already for sniper so again she does have to bring a separate you know separate unique game to the table for us to be able to pull for her and use her but I'm gonna be pulling for her anyway so anyway guys thank you very much for staying this far put your comments in the comment section take care stay safe this is The Warden and I'm out of here


This is stock number 80106A. We are here at Summit Automotive, your new and used Ram ProMaster and ProMaster City headquarters. Today we are checking out this super clean 2015 Ram ProMaster City tradesman edition. This vehicle has the 2.4 liter four-cylinder engine which pumps out right around 184 horsepower. From this HD video you will be able to tell just how clean this van is all the way around inside and out. Continental counter pro contact tires, two 1555 R716 tires and they are on the steel rims and it does have front wheel disc brakes. Those tires have a lot of tread left, I'd say close to 60%. Dark red is the color. Front bumper absolutely perfect, no scuffs, no scrapes, no dents, no dings, no cracks. The hood, excellent condition as well, no scuffs or scrapes or dents as well. Very very clean, passenger side rim, excellent condition and as we go down this side of the van you can see just how clean the body is, how reflective and glass like that paint is. We take these HD videos so if you are far away or even if you're close by and you just cannot make the trip down but you want to get a good idea of the overall condition of the vehicle it's why we do the video. So you can see the video, you can look at over 25 photos, you can make a smart buying decision from the comfort of your own home. Back rim is an excellent condition and the back tires have just as much tread as the front tires. I really like these ProMaster cities personally just because of how easy these doors slide open. I mean it literally is almost no work to open and close these things which is nice because if you got your hands full you want to make sure it's easy to get in and out of. Inside we have a really nice rubber flooring that's in here, you got some tie downs as well. Back there is the wiring for the receiver hitch that this vehicle does have. 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These doors also have the feature to go even farther open so you can do it like that so it's 100% you know flush. The other reason is that they make this door smaller is that is the curbside doors so if you got to get stuff out you don't have to walk around as much of a door area when you're doing that so very cool stuff that they got on this ProMaster these back windows are tinted and have the rear defrost in them now this has a topper ladder rack on the top it's a really nice looking ladder rack heavy-duty and you can probably haul quite a bit of stuff up there also in nice condition and as we go down this side of the van just as clean as the passenger side I didn't see any major dents not really many any and not many dings or anything on it very nicely taken care of inside the tradesman package gives you the two-tone gray cloth interior you do get side curtain airbags on these seats weather tech floor mats throughout power windows and power locks and you can see that this van does have 7,000 726 miles on it so very nice instrument cluster there you have the outside temperature display there's a mileage display as well and the time inside you have the M FM and Sirius XM radio capabilities on the 5.0 touchscreen radio Augs and USB jacks the 9-speed automatic transmission center console shifter there you got a little bit of storage up top there and a glove box weather tech floor mat on the passenger side and that passenger seat is very clean as well finally there is a little storage compartment up here which is a nice feature if you got a haul or if you got papers or stuff that you need to haul around with you they can go up there steering wheel is in excellent shape you got Bluetooth audio controls on that side and you have cruise controls on this side we'll take a quick look under the hood has manual mirrors under the hood we have the 2.4 liter four-cylinder multi-air motor engine bay is very clean runs very smooth this van has been fully safety and inspected by our Chrysler certified technicians has a fresh oil and filter change all the fluids have been checked and topped off the vehicle has been gone through mechanically 100% and this van is 100% ready to go super clean if you're looking for a nice little work van this is definitely highly recommended and to see more pictures of this van or one of our other 400 new and used cars trucks SUVs minivans Wranglers you name it we got it go to our website www.SummitAuto.com full pictures and descriptions of every single vehicle on our lot videos of every single used vehicle that we have all at SummitAuto.com thanks for checking out the video if you want to make this one yours give us call<PHONE_NUMBER> asked for one of our sales associates to make this van yours today once again that number is<PHONE_NUMBER> thanks again if you want to check out more videos go to youtube.com slash summit auto remember to like subscribe and share on that all those videos and this one in fact any second you see a link to subscribe to our YouTube channel on your left a link to more used inventory vehicles like this one on your right if you haven't been to our website on the bottom a link to this van on our website click those check us out and we really look forward to help you with this super clean 2015 Ram ProMaster City and if you don't like the color of that one or want a different one we have new ones in stock as well all at SummitAuto.com thanks again


Hello everybody, HoodedCoverCommander788 here, and it is time for another Patreon poll. It's been a long time since we've done one of these, and they are always fun. For these polls, I give patrons three options, and they decide what will be reviewed on this channel. You do need to be a supporter on Patreon to vote. Support at any level gets you the right to vote. The link will be in the description of this video. Supporting the channel on Patreon helps me continue doing this work. I could not do it without your help. Now let's make the world safe for democracy and see what the options are. Option 1 is 1991 Ozone Version 1 from the Eco Warriors. It's been a long time since we've looked at the Eco Warriors, so that could be fun. This is a fairly popular character as Eco Warriors figures go, if it's possible to measure such things. If you would like to look at bright colors and crazy gimmicks, select Option 1. Option 2 is 1992 Mutt Version 3. This is from another 90s subteam, the Drug Elimination Force. I'm actually somewhat partial to this figure. It's a late version of an early G.I. Joe character, and one that may be overlooked. If you would like to learn how to say no to drugs, select Option 2. Option 3 is a very popular character, but a version of that character that may be lesser known. This is 1993 Duke Version 4. You can't get much more popular than Duke. This is 1993's attempt to do a realistic military figure, and I know some of you like that kind of thing. If you would like to see a 90s version of Duke that is not an astronaut, select Option 3. There are the three options. Once again, you do need to be a supporter on Patreon, but support at any level gets you the right to vote, and it helps me out a ton. The poll will start as soon as you see this video go up on Monday, March 13th. It will end on Friday, March 17th, and the winning review will publish on Sunday, April 9th. Once again, the link is in the description of this video. This is something I need to do more often. The supporters on Patreon really make this channel possible. I could not do it without them. So go ahead and get voting. The poll starts now.


At Audiology Associates at Prestonsburg, you can live your life the way you want and find the freedom of better hearing. You'll experience patient care that is specific to you with exceptional follow-up care that ensures your hearing and balance needs are being met. Audiology Associates at 1428 Northlake Drive in Prestonsburg. During the holiday season, many may be tempted to post their memories to social media, and while there are many memories that are worth sharing, others may pose a threat to your holiday cheer. We put together a list, you know, sort of an article to let people know some tips, some quick tips on how to be safe over the holidays with social media. And one of the top ones you might imagine is don't post photographs or words about your very expensive presents. So if you're lucky enough to get very expensive presents, it's tempting to let all your friends know and everybody know. But it's really not smart because your friends may have friends who aren't so friendly. They might see it and see it as an opportunity. There are many different kinds of scams on social media and being aware of the website you're on and the people you're talking to is a key part of keeping your information protected. If a social media post asks you to reply and asks you for information like a telephone number, obviously like a social security number, or any other kind of bank information or your Venmo, don't trust it online originally. Just don't stop and make sure that it's something real and usually it's not something real. Legitimate places will often not ever ask you for things like that, right? As the holidays approach, no one wants any bad tidings at their door. By being aware of what you post online, what websites you're visiting and the people you're speaking to, you can keep holiday mishaps from ruining your Christmas season. For Mountain Top News, I'm Kelcie Dean.


Hello everyone, welcome back to my channel. I'm Dr. Ahmet Ergin. I'm an endocrinologist, a diabetes educator and a metabolism expert in today's episode We are gonna be talking about the top seven best to go foods for diabetics Let's jump right into it. This is John, a 45 year old middle-aged man Recently John received some unwelcome news from his doctor He was diagnosed with type 2 diabetes Due to that news, John now has astounding perseverance and determination to incorporate healthier options in his diet Unfortunately, like everyone else who is on a diet, everything looks a hundred times more tempting Plus, John leads a hectic life Early in the morning before the rooster crows, he is at work and he stays in his office till ungodly hours late at night when the moon is already in full bloom Because of this busy lifestyle, John barely has time to cook healthy food and must depend on a take on almost always or delivery What John needs to know is which types of to-go foods are healthier options for him and for his family so that he can take control of his diabetes and obtain better outcomes But we should not be eating out when we have diabetes. We know that, right? But I'm sure many of us still get meals from restaurants and you still would like to know which foods are Part of that waiting to happen or which foods are more wholesome and nourishing for our bodies Let's get right into our list of some of the healthier, not so fully healthy But to-go food options for better blood sugar management That's if you have to eat outside First up are chicken wraps. Wraps containing whichever protein you prefer Well, of course, the perfect scenario would be finding a shop that actually sells wraps that are whole grains or a fiber tortilla or a low-carb type of bread I know some may think that the grains are bad for you But if you strongly believe that that's the case and they spot your blood sugar just go with the lettuce wrap Well, not every waiter will also understand what you're asking But there's no harm in trying, right? One popular fast-food chain, which is Wendy's for example Sells a chicken wrap with flour tortilla, which is a lower carb Ideally the wrap should contain lean protein like chicken breasts and a reasonable portion of vegetables Let me ask you all, who doesn't love a takeaway from a favorite Chinese restaurant?  You know, these are these are usually fried Before being coated in their sauces. Now these sauces are also They contain a lot of amount of sugar in addition to that just remember how much rice you're eating next to it because they always Come with the rice right Chinese food Try to choose with go with the brown rice or if possible, no rice Well, how about the pizza who can hate pizza, right? The abetics usually steer clear of this tempting Monstrosity, monstrosity It's easy to understand why right pizzas are generally known to be loaded with fats and sugar and carbs Well, that's generally true. Let me tell you a secret though That depends on what type of crust and toppings you choose as well as the portion size. If you have to have pizza opt for a Thin crust, whole wheat, high fiber crust Whenever possible as this is healthier than those thicker the deep pan or cheese stuffed crusts Choose vegetables like mushrooms or spinach for the toppings instead. Well, you might say Pizza isn't pizza without meat though, you know Well, that's kind of true And if that's the case for you go for lean meat like a chicken breast if you didn't know Yeah, they put chicken breast on pizza That will be a lot healthier and a delicious option stick to one slice only if you have to have pizza though You have to control your portion now. I'm gonna move on to the Japanese food guys Let me tell you a cool trick Drink miso soup before everything else. The research has shown that people eat up to 20% less This way because it tricks their brain into thinking that you aren't as hungry Well, the soup theory is not specific to the miso soup, but soups in general are filling So it's a good way to start your dinner for example, and then move on to the main menu It contains a lot of sodium some soups So you have to be careful if you have to have that soup outside You have to consume much less salt rest of the day or as for a low salt option And don't order sushi with fried foods like tempura that are rich and creamy like mentai sauce Instead opt for healthier options like salmon tuna and veggies Salmon and tuna are rich in omega-3 and this fatty acid not only improves your heart health But can even prevent the nerve-related problems such as diabetic neuropathy Lastly add a salad to your meal for that extra boost of fiber Some of us aren't very intuitive when it comes to using chopsticks So using these might actually help you slow down when you eat Which is an important part of technique to prevent overeating next up. We have Indian food Now this could seem like a minefield, but hear me out Dal for example contains lentils and lentils have to regulate the blood sugar levels like we discussed before They give you a better glucose control with less spike can even lower your blood pressure So dal can be quite oily though So you have to consider sharing the portion and go for like a chapati or roti instead of a non-bread if you have to have bread No, hopefully not Because these generally contain less carbs. So if you're craving meat, tandoori chicken is reasonable The chicken is coated in a light yogurt sauce that is spicy and then grilled before serving A healthier option for a diabetic, I would say. If you're a sandwich fan Then we have some great tips for you as well. Usually it will be best to ask for only lettuce if possible If not, half a portion of the bread or at least you can ask for a whole grain version of that bread Also up for loads of veggies like lettuce and lean meat like a chicken breast And then chicken breasts might not be as delicious, but it's definitely miles healthier than processed meats or meatballs And believe me, your taste buds will eventually change. You're not going to crave for those processed meats anymore According to American Heart Association, the processed foods were discovered to account for up to 70% of the daily sodium intake of an average American And not to mention the nitrates in there Because the diabetics are more prone to high blood pressure You have to really watch for your sodium as well, not just your blood sugar because your heart attack Your stroke risk, your kidney disease is also dependent on the sodium blot as well You can also choose mustard instead of mayo in when you go out and low-fat mayo is acceptable Let's talk about burgers Well, they're fast. They're convenient and it's a popular to go for it, right? So the million dollar question is what is the difference between a healthy burger and an unhealthy one? A good question, right? Well, there is really no super healthy burger unless you go for like a turkey or salmon burger If you have to have one, just go for the healthier options Try to ask for smaller sized whole grain buns or some English muffin or something, right? They generally contain less carbs And if you're feeling adventurous, try a burger without the bun with some lettuce instead of like Use the lettuce to sandwich them instead of bread As you mentioned earlier, try to avoid full-fat mayonnaise and they contain just a whopping amount of calories Even if you're in a high-fat diet, that's gonna put you over the calorie limit And of course, leaner cuts of meal will be your best option So I hope these are healthier options I hope you learned something today Undeniably takes a lot of commitment and perseverance Especially when there's an insane amount of not worrying foods out there Let this be a journey we embark on together for a healthier me and a healthier you Thanks for watching guys If you enjoyed this video, don't forget to like subscribe and share this with your family and friends Let us know in the comments below what you would like to see in our next video until then Take care and I'll see you in the next one All right. Thank you for watching and I want you to be more informed and more educated So to do that, go ahead and watch this next video right here


Andrea here with Dental Health Tutoring. So if you're taking the board exam this month or very soon, you might be wondering, so how can you really make sure you have studied all that you possibly can? You've taken the mock exams, the case studies, how to know if you are truly prepared for the board exam, or perhaps you need to study a little bit more. So what I suggest doing is I want you to take more mock exams and more case studies and really determine so how did you do on those mock exam and case studies? I'm not talking percentage per se, but let's say you didn't know a lot of the questions for radiography, then that tells me you should go back and study radiography. Maybe not all of radiography, like maybe you knew a lot about the history, but you didn't know a lot about the tube head, or maybe you knew a lot about the tube head and the components, but you didn't really know about processing errors. So go back and study what you had a harder time with on the mock exam. So depending on if you have a couple weeks left, a month left, a couple days left, you want to study very strategically, meaning you don't wanna study things that you already know. So this goes back when I tell people, when you're first starting to study for the board exam, let's say you have three months left, study everything because you don't know what you don't know yet. If you only study what you think you don't know, you're leaving behind things that you need to be studying and refreshing your knowledge about. But if you only have a week left, if you only have a couple days left, there's no point studying what you know you know, like the easier stuff. Like maybe you know all about tooth brushing methods and you don't have to review that. Don't review something where you know the answer already, but go back and review things like pharmacology, the more common types of drugs for pharmacology. Maybe you need to go over those medications that are in a medical emergency kit, the more essential ones. Maybe you need to go over dental procedures because you don't remember the main instruments that are maybe an amalgam procedure versus a compassive procedure. Do you know the different types of crowns? You know, metal, porcelain fused to metal. Porcelain, zirconia, do you know these types of things? So study what you don't know because anything new that you learn before you take the board exam, especially if you don't have a lot of time, you will retain that information a lot better. If you have three months to study, that's perfect. You have tons of time.  But if you have a couple weeks or a couple days, you don't have time to go over the whole textbook again or anything like that. You don't have time. So really focus on mock exams and case studies and watch session recordings also. Watch teaching videos because you might be burnt out at this point where you have studied so much, you can't read anything else. That's okay. Go inside and look at our past session recordings where I go over one topic for one hour. Just listen to me talk while you're even in the car running errands. You're folding laundry. Your kids are watching a movie. Well, you can put your, you know, air pods in and listen to me teaching you something else. So try to be very strategic with it. But don't think you know everything and that a week before, you don't have to study for the exam. Please keep studying because you will be kicking yourself if when you take the board exam, you forgot a question or you don't know a question. You'll be kicking yourself and thinking, if I had just taken 15 minutes to read over the essentials that are in a medical emergency kit, I would have gotten that question right. So don't do that to yourself, okay? So good luck, you guys. Reach out if you need any help. I want you guys to stop studying eventually. So when you take the board exam, you'll feel so much better that you don't have to study anymore, but you'll have another stress wondering if you passed or not. So let's get you to pass, okay? Let me know if you need anything. Thank you for watching and I'll see you guys in the next one.


to everyone at our talk at the OpenAI plus data forum. I'm Shwai and with me is Rishith and today the topic of our presentation is federated machine learning with the help of givenities. So a very quick introduction about ourselves. I'm Shwai I'm a developer advocate at Millie search and also contributor at layer five, which has a few projects like misery that come under the CNC landscape. Over to you, Rishith. Hello, I'm Rishith. I'm a high school student and an incoming student at the University of Toronto. I'm pretty excited about machine learning and most of the time we'll find me creating machine learning open source projects or contributing to projects including but not limited to Kubernetes, TensorFlow, and more. Well, over the past decade or two machine learning has kind of become the central point for a number of different applications around a huge variety of domains, and it's probably unimaginable to find a domain where machine learning or data science might not actually being used. So, fields ranging from healthcare to autonomous vehicles have been transformed with the help of machine learning techniques and machine learning importance specifically within these real world applications has brought to us, brought us to a really new field as well. And that's MLOPS, because today machine learning is not just for use case like you know being able to write a model and then use the inference machine learning is also being used in production. And that essentially brings the point of how we can actually leverage the use of machine learning with the help of DevOps as well to make machine learning production ready and make it scalable at the same time. And that's where being able to actually run machine learning not just in larger applications, but also in very small resource limited applications as well. And that's what we are actually going to be exploring in today's presentation. Over to you. So, interestingly, it might be, it might be a really nice time to put it over there that a lot of data is created on the edge, take your standard smartphones for example, and if you think about it, a lot of the data that you might want to train your machine learning model with is actually created on the edge. And you would want to be able to leverage all of these data that is created on the edge. And traditionally how machine learning systems have hope is you have an edge device, which has all of this data, and there is a central model on the server. The model is being run on the server, it sends any predictions to your mobile device, mobile devices in our case, a representative of any edge device. So it sends the predictions to your mobile device and the mobile device with the data that is created on the edge would probably send feedback to the server, and server would probably re-drain the model. So this is a very standard approach that has been used for a lot of time. And this has worked for a lot of applications as well. And you just scale it out and you get multiple edge devices, multiple feedbacks, the server is retraining on all of that data. But with the centralized approach, there also come the questions of how good is it in terms of latency, because each time you are making a network call, you also require network for that, because you're probably making an HTTP call. What about privacy? Sending user data directly to the server so that in terms of feedback, so the server could re-train on it. And this is also highly power consuming. So all of these shortcomings with the traditional approach is what led to something we call machine learning on the edge. Yeah, and that's where we talk about how our primary goal actually being that how we can actually balance the accuracy, but also at the same time, we are aware of the resource constraints that come with machine learning on the edge. Because of the factor of limited resources and compute power, the goal is to optimize the accuracy with the runtime resource consumption as well. Now, of course, typically you might look at this problem with a couple of different steps. So the first approach that you could actually take is to actually take the existing larger models and then try to compress them down in size that are more better focused towards being able to actually run on the edge. Now, one of the other techniques that comes to mind is essentially kind of a bottom up approach where we take new math and we actually build these machine learning models from scratch where your predicted classes are actually specially designed for these resource constrained environments and they are much more well better suited for smaller competition footprint. But of course, the approach that we are seeing today is that how we can actually take the tasks that help to compute our machine learning models on the edge by first essentially taking some of these tasks that can actually be computed on the edge itself while sending some of the more resource intensive tasks directly to a cloud data center. So being able to actually combine both the being able to actually do the computational directly on the edge device itself while sending the rest of the data to a cloud server where it can be done there and then of course you're managing the results that come from the cloud and then are interpreted on the cloud itself. And this actually helps us to use this in a number of different real world scenarios. And there are actually some existing applications that are utilizing machine learning on the edge including things like a lot of voice devices such as Amazon Echo or Google Home and then of course they're being utilized in a number of different educational and healthcare applications such as being able to actually utilize machine learning on the edge with predictive sensors. And in the future, we might see being able to actually use machine learning on the edge for things such as being able to manage your patient heart, heart levels, glucose levels, being able to use them in cameras, motions and sensors as well. And of course the main idea is to actually process most of this data locally on the edge to also provide more safer inference as well. This paradigm talks about inference on the edge and well that solves one part of the problem but today we'll be talking mainly about as you might have guessed on the title and now we come to federated learning and we talked about how we went from centralized training to inference on the edge but federated learning takes a step forward from there not just inference on the edge but we pose the question can we do the training on device because in the inference on the edge scenario still we'd have to it only solves a part of the problems. A lot of the data that is created on the edge needs to you need to train a model with that. So, okay, so how do you do training on device, because training is one of the more computationally intensive tasks. And you could, and there are a couple of problems you face with, let's say, just taking all of your training code which was on the server earlier and putting it on your mobile device. Mobile device is the representative of any edge device in for the purpose of the stock, but just putting your algorithm which was used to train a model on the server on a mobile device might not work due to a couple of problems. First and foremost, there is often too little data. A single edge device will often create a lot of too little data to train a proper machine learning model. And there also comes a problem that other devices are not contributing. So, you probably have deployed your machine learning model to multiple edge devices. None of those other devices are contributing to the improvement of the model. Each of them are probably training their own model in this setting. So, the answer to all of these questions and also the answer to can we do the training on device question is federated learning and incomes federated learning. So, a very high level overview of federated learning and then we'll of course move to demos seeing this in practice. But what we want, but in the case of federated learning what we want is multiple clients or edge devices to collaborate and learn a combined model, because you also want probably the other clients to have an impact on the model and edge devices running. But how and this would probably be coordinated by central server, but then comes the question, how do you preserve the privacy because you don't also don't want the raw data to be shared. So, that is something we need to take a look at. And let's see how we can not share raw data but still do federated learning and do the training on device while also being able to use the model being trained on data by other devices or other clients. So, the way this works is, well, you have an initial model on the server. And you also have that initial model on your edge device. The edge device trains the initial model with a little bit of data it has. So, there you have the locally trained model trained with very little bit of data that is collected on the edge device. And this is a very simple task so you can probably do it on on the edge device very easily. And right now let's just say the locally trained model is being shared. But what actually happens here is the updates from the initial model to the locally trained model are actually being shared. But if you see that the raw data is not being exchanged at all. So, the locally trained model would come to the server and you'd probably have this on for multiple edge devices. All of them would send in their locally trained model and no exchange of data. And you'd probably apply some aggregator function to create a collaborative or combined model using learning from all of these. And if this seems like too simple of an idea, well, you have to probably do it multiple times to get to learn from the data collected by all of these. So, the combined model now becomes an initial model and you repeat this process again. So, it is pretty interesting how federated learning in a moment just tackled all the problems we had with being able to train on the device while maintaining privacy, speed and all of that. So, for the motivated reader you could actually take a look at this paper from 2018 about how federated learning is being used for Google Keyboard. It is a pretty interesting paper on how they use federated learning in Google Keyboard at scale. So, that might be something interesting to see for the motivated reader. So, what we'll be talking about today as the demos will be seen today will also be about TFF that is TensorFlow Federated, which is an open source framework for machine learning and other computations and decentralized data. So, we'll also be taking a look at TensorFlow Federated and though we talk about machine learning all this time, you are not limited to machine learning. The idea of federated learning, the idea of applying computations to distributed data without sharing the data to a server without sharing or without getting the data out of an edge device is not just limited to machine learning. And there's a ton of things you can apply. The ideas we talked about today for one, analytics is a very booming field where federated algorithms are very commonly applied and it's well not machine learning. So, you can definitely think the ideas we talk about and we'll show in the demos to computations which are not machine learning. So, we'll be taking a look at TensorFlow Federated as well as how you can use Kubernetes to simulate multiple devices in the demos. So, now we come to the interesting part that is demos and let's get on to that. So, now we'll be looking at a federated learning demo and we'll try to create a federated machine learning algorithm using TensorFlow Federated. And this will not be a deep dive and we're essentially just using the federated learning API is to give you an intro to federated learning. If you are more excited about federated learning or probably want to add in your own computations, you could definitely build on top of what we show in this demo. But due to time constraints, we'll actually just show a minimalistic demo of training of federated machine learning algorithm. A minimalistic but complete demo of training of federated machine learning algorithm. So, let's start out by getting the input data. So, in this example what we'll be doing is we'll be using the MNIST dataset and there is essentially an E-MNIST dataset which is built for federated learning. And you'll see why this is built for federated learning. But something we'll be doing is we also want to see how federated learning datasets might be different. And so this is just a famous MNIST dataset but in a federated learning environment. So, the MNIST dataset is essentially a set of hundred in numbers and your goal is to create a machine learning algorithm to classify them. And what we want to see is there are, so in our case we now have a lot of different clients. Each of them have their own dataset locally collected dataset because as we talked about a lot of data is actually created on the edge. So, let's take a look at the datasets we have and if you see like all of these datasets, all of these different colors of graphs of bins actually represent different numbers because it's a hundred and digit dataset. Not all of them have the same number of examples and this is very much expected because not all of your clients or edge devices would have the same kind of data. Another is this example. So, this is like a mean of all the images in the dataset and just as an example, if you see the dataset that client one has two over here and the two on the dataset that client two has is very different. Those are just two different styles of how people write to us and this is because the data is collected locally. So, as a user everyone would have different styles of writing to and so the federated learning datasets for all of these clients are actually pretty heterogeneous. They are pretty different in their style, they're pretty different in the distribution of the data and not all of them even have the same number of examples. So, this also in a sense just shows you what we talked about why is there a need for collaborative learning, why other edge devices should also contribute to the machine learning model. Well, just to show you the datasets on different devices are actually different. Next, we want to pre-process the data and this is essentially very simple steps. What we are doing is creating a set of two sets of tensors. Essentially, X tensor which contains the pixel values of the image and a Y tensor which contains the labels. So, this is a 28 by 28 image. We are just converting it to 28 by 28 image to a 1 cross 784 image. You already know you can simply convert a 28 by 28 image into a 1 cross 784 image very easily. So, that is what we are doing over here. We are also adding them into batches. So, we are also adding them into batches of datasets. So, that is all that we're doing this pre-processing set. And if you see, yeah, this is actually one batch of dataset we have from a particular client. This is one batch of the dataset. We have the X values. All of these are a single image. This is a 1 cross 784 image. This is a 1 cross 784 image and so on. All of these are the Y values which just represents what is the number of... Oh, what is the number of... Oh, what number it is in the image. So, a very simple event till now. And we'll just pre-process our data in this sense. What we do next is... So, if you remember we earlier put in that we want 10 clients. So, what we have is essentially 10 different datasets. So, another thing I just wanted to put out over there is in an ideal scenario, you would probably have multiple devices, probably a thousand devices. And then you would choose 10 or so devices out of it. But in our case, we just have 10 datasets. So, we are assuming that the selection process is already done. Ideally, you would want to do the selection process somewhere, let's say a mobile device. Again, this is the representative of each devices. But let's say when a mobile device is being charged and is not on a high-speed Wi-Fi, connected to a high-speed Wi-Fi, that might be the best time to train your model and send out the updates of the model to the server. Since it would be not a very big constraint on the mobile at this time. It is already being charged and you also have a decent connection. So, ideally you want to select your devices this way. But in this example, you are thinking that, oh wait, you already selected your devices and now you have to send datasets. The dataset to a model part would be done on the client itself and the client would then just send the updates. So, we'll get on with that part and we'll just think that there are only 10 clients. All of them have their own dataset, which we just created. So, now you can actually define a simple curious model. So, of course, you can modify this as well. And one of the things you can also do is not just use this idea for federated machine learning but all kinds of applications and distributed decentralized data. So, you can also implement them with TensorFlow federated. You can implement other kinds of computations or distributed data as well. And you don't necessarily need to have a curious model. You can customize all the computations and go in as well. For the purpose of this example, we'll simply use a curious model, a very simple neural network. And we'll wrap it up as a tff.learning.model. So, this method actually creates a TensorFlow federated model out of this. So, for a TensorFlow federated model, it will take in a curious model and then create a TensorFlow federated model out of it. Or you can think of it as a wrapper for our simple curious model. Of course, this can be any model you want. Here, it's just a simple neural. So, the other important part was the federated averaging algorithm. So, if you think about it, we actually have two optimizers over here. So, as you might have already guessed, one of the part of getting the updates from the trained model is on the client side. On the client side, you train a model, then get the updates and send those updates to the server. The server then also has to create a new model, which is represented on the updates which has gotten from all the other devices and apply them to the initial model it had on the server. So, it needs to apply these updates to the initial model it had and that would be on the server, which is why we have two optimizers over here, two different optimizers over here. So, as an example, if you just see... So, the TensorFlow federated actually puts all of these into a single iterative process. And what you can also see is... Let's say this is for the... So, this is a simple example of what things are there on the server. So, the server essentially has a global model, the weights for the global model. It has the distributor, which would be required for the client-server interaction. It has the aggregator and then the finalizer. We already talked about these components earlier and now it might come properly into the picture if you remember the diagram. So, that is what we are doing over here. And so, we also have... So, we can do a simple round of it. So, we can do a simple round of it using .next. That runs a simple round. So, how does a round go? A round goes... If you remember the very first image when we saw federated learning. So, a round takes the initial model, gets the updates from all the different devices, and updates the initial model on the server. We probably need to do this multiple times, but that is how a single round is here defined. So, we actually ran a single round and as I talked about earlier, you probably want to do this multiple times, right? So, that is why we'll just put it up in a for loop. Yeah, as simple as that. And run this round for multiple times. If you actually see over here, if you see the losses over here, the losses are actually decreasing and the accuracy of the model, the model being able to classify an images is actually increasing. This is just now for 10 rounds. You could do this for a lot more rounds and it would, of course, increase by a lot more. Let's run this again for 10 more rounds. So, if I just run this again for 10 more rounds, I can see that the accuracy is still improving. I just run it for a single round. Sorry. So, let's run it for 10 more rounds and I can see the accuracy is actually increasing. The losses are actually, the loss values are actually decreasing. So, the model is improving over time and this is happening from 10 different clients. None of them are actually sharing the data they collected on device and we are still able to create a better model, taking the learnings from all of the data that is collected on device. So, this is a great introductory example to federated learning and next up we'll see how we can run this simulation in a Kubernetes cluster. So, next demo we'll see is how you can simulate this for a number of devices in a Kubernetes cluster which also makes it easy to simulate this run federated learning algorithms. So, we'll actually use a pretty similar code that we used last time. It's essentially the same neural network and essentially everything is almost the same. So, you might see that this is a pretty similar code as the last one. So, we'll use the similar code as we did in the last time but this time around we'll also try to simulate the same model, the same training of the model in a Kubernetes cluster. So, let's try to do that and the first thing I'll do is I already have a Kubernetes cluster. You could create this anywhere you want and there it is. I have three nodes on my Kubernetes cluster already up. So, first what I'll do is I'll create a deployment for the TFF workers and this will use the TensorFlow federated and they're also providing image. So, you could do remote execution very easily. Essentially, you remotely execute the federated learning algorithms that we'll probably write over here. So, every time like you make a round, all of that will happen on different instead of the clients that we had on our own system. I'm not even clients just different datasets. This time around we'll have a proper simulation to do that in conflict as a simulation in a Kubernetes cluster. So, we'll create this deployment to start off with and this has actually been created. So, what I'll do now is also create a load balancer and we can probably use this. We'll now use this to run our federated learning algorithms. So, this shows me that my load balancer external IP is still pending. Okay, now we have it. So, let's get our IP and we'll actually use CRPC to run our code so I have to run the federated learning algorithm just as we said earlier. So, there it is. I have in my IP address and I'll actually do the same example, create 10 clients but this time around it will be much like a proper simulation. So, all of this happens in a Kubernetes cluster. So, let's run this and now we'll run the evaluate function. So, what the evaluate function is is essentially running the one round. So, the evaluate function runs all of these rounds and let's say run it for 20 rounds. So, we'll now run the same algorithm. We had earlier for 20 rounds. So, let's execute this again and then we'll just call the evaluate function and this time around, as I told, this is actually running like a proper simulation. The evaluate function for you for 20 bucks, it was taking quite a bit of time so I just split it up and there you have it, a simulation of the same code, the same model for 20 rounds and Kubernetes. So, that was about the second demo. All right. So, thank you so much for attending our talk. So, in case you have any questions, do feel free to reach out to us on our Twitter handles how to help and thanks for taking and in case, of course, now we are open to questions. So, we'd like to take up any questions and of course, we would love to see you next year in person at Open Source Summit Latin America. Thank you very much.


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