Genevieve AI is one of the hottest hobby right now. But what does it look like to be able to become the head of AI for a global company and working on Genevieve AI and also lead an amazing team to achieve greater purposes? And what does it look like to be able to move from Brazil to UK with visa sponsorship and really create an international career? There's a lot of new opportunities that Nicholas created for himself, for his family. And in this video, our guest speaker, the head of AI, Nicholas is going to share with us how he created international career as the head of AI and it saves the children international. Hey, guys, this is Dr. Nancy Lee, a director of product and feature in Forbes that helped 100 people learn the dream PM job offer in fan companies, a unicorn startup and continue to get promoted as a product leader. In this channel, we cover tech trends and free product management training. Like and subscribe and check out our new video every Tuesday. And today we have the head of AI, Nicholas, saves the children international in UK to share with us how he created international career in this very hot tech topic right now. So now let's welcome Nicholas. How are you, Nicholas? Thank you for joining the show with us. Oh, so thank you for inviting me, Nancy. I'm very happy to be here. Awesome, it's such an honor to have you on our show. So Nicholas, can you quickly introduce yourself to the audience? Oh, hi, I'm Nicholas and I'm a 10 years of experience product manager in the AI field and digital health. Right now I'm working at the Save the Children International as head of gender TVI, an NGO that impacts more than 50 million children around the world in more than 100 countries. So this is my dream job right now. So I'm very happy to be able to deliver the state of the art technology for good without having to spend so much brainpower on how are we going to make money out of this? Awesome. So Nicholas, and actually I was very inspired by many even things in your career. First of all, it's your immigration journey and second is to actually become the head of AI. So let's talk about your entire journey based on my understanding of your background. Actually you only were educated in Brazil, you're Brazilian, you never worked outside of Brazil. And right now your company sponsors you to move to UK. Can you tell us how did you actually become the head of AI in a UK based company as a Brazilian immigrant? So I was struggling a lot with how to organize, LinkedIn, resume, everything like that. So I was two months looking for opportunities and struggling a lot to get interviews. Then I found out about the PMA and then I was able to organize everything, understood what I was doing wrong. And the key thing was to understand that being a great professional was completely different from being a great candidate. So I needed to understand how to play this game of a candidate, how to bring myself better, how to communicate better. So all of this I learned. And after getting set the core things, I was able to get a lot of invites for interviews, dozens a month. And I was able to choose very carefully where I would want to be. So that's when I recruited from the State of the Children International approach me. And it was very interesting because I was in a lot of interviews before for U.S. And one of the problems I was having is that the recruiter asked it if I was in U.S. already, but I was in Brazil working for a company based in U.S. And another interesting information is that a lot of whole girls had the information that we need someone who sees diversity as something has a power, a strength, as something important, okay. But I found out that most of them don't just write this on paper. So Save the Children International, I'm working one month already and I see the client is in practice. So I'm very happy to have found a company that truly sees diversity as a power. So talking with the recruiter, it was very interesting because I decided to be myself and be more humane with her and show my passion for doing technology for good. And then she understood that I was, they were not looking for a candidate from somewhere outside U.S. So they were looking for the best AIPM candidate from U.S. And they were able to find me. And then in the interviews, the recruiter understood that if I were the best candidate for the role, it doesn't matter where I work. So everything worked out and the institution struggled a lot with bureaucracies to get me to UK so that they can hire me. And I really, really truly appreciate that. So it was not an easy journey, bureaucratically wise, but we were together on that. So I felt really embraced it. Awesome. So right now you're already starting your job and a sponsor visa. I also heard that they're gonna sponsor a whole family to move there as well, right? To UK. Yes, yes. Not the whole family because the cats are the additional things, but the visa process, the travel to get the biometrics, everything. So I'm really glad. This is awesome. Such inspiration. Also the first month of expenses. So I truly appreciate that and that's it. Poor cat. She needs to learn British accent before she can move over. But I'm so glad the company actually discovered that you are the best candidate, regardless where you're based. And then they just need to create the visa sponsorship and the right opportunities for you to move over to UK. It's such an inspiring story for everybody. Now, Nicholas, let's also dive deeper. You said at the beginning, your challenge was your private jobs online, nobody give you opportunity because you're in base in Brazil or maybe your resume wasn't well written, maybe has nothing to do with Brazil, right? So tell us what kind of challenges have you faced when you land your next job as head of AI in an international company? And how did you conquer those challenges? At the very beginning, I was trying to go get into the interviews and then try to have the conversation. And after that, when I asked it, if I had the H1B visa or everything, then, yeah, say the truth, of course, I don't have it. I work remotely, I work for a company remotely. And I spent a lot of time with that, with these interviews. What I changed in my behavior was, I started thinking that maybe one curious case was a recruiter that approached me and then called me on the phone to interview. And instead of interviewing me, he spent the first 15 minutes of the one hour we had together, just selling the company, saying, this company is so good for you, your experiences are great, you're the best person, we need you to start right now. So he spent 15 minutes just selling the company and I was like, uh-huh, uh-huh, okay. In the last minute, he asked it, okay, so just to be sure, are you right now in US? Are you a US citizen? Then I said, no, I am not an US citizen. I am Brazilian citizen. I work for a company that's based in US remotely.  Then that changed some buttons here on my brain and started thinking, well, maybe I just losing time with this, so I need to get into a company or place or talk with people that truly appreciate the intercultural improvement that hiring someone from outside US can bring to the company. Yeah. And then I stopped going into all interviews and then I started saying right away, I'm Brazilian, I don't have the H1B and I don't have citizenship. And then I started after setting everything up as I learned in the PMA course, I was receiving a communication from recruiters two to three times a week. So they sent sending rows directly to me. Then I started just saying, I'm not a US citizen. If this isn't a problem for you, let me know, we can schedule a meeting. So here's my resume, okay. And then I filtered out 90% of propositions, saved a lot of time and went into real opportunities. I found out I had some offers from companies that were interesting in the health tax sector, but not exactly what I wanted. So I just waited a little bit more and when the recruiter from Save the Children approached me and then I learned about the organization I fell in love with Insta, I fell in love with the story and this organization. It's a 104 years old organization with 17,000 people. And before I was in a startup, very fast for Agile with hundreds and a few people. So it's a big change for me. And I'm learning that they really see with good eyes the PM experience. This is awesome. So basically conquers the first challenge in terms of visa, people do not embrace your international experience. Can you also tell us more regarding how you land ahead of AI role? Because the two challenges, there's lots of even Americans, they don't need visa sponsorship. Most of them couldn't land ahead of AI role. So how do you actually climb up to become the head of AI? Especially you're just senior PM, right? So now we become head of AI in a such large international organization, move you to UK, you must be much better than other candidates. Can you tell us more regarding how you made it happen and how to really make you stand out compared with other candidates? Okay, so I always focus a lot on impact. I think there is a big hype around AI always head and people think that displaying ability with algorithms and techniques will make them a good AI PM. But that's not the case. To be a good or a great AI PM, you need to be always focused on the problem. You're trying to solve in the impact you're going to generate with that. All the time question ourself, am I solving a problem or am I looking for a problem to use my tool? So AI should be the last tool you decide to use because of the complexity with data versioning, with software versioning, with how the user is going to interact with it, the UI, UX around the AI is very hard. So you should always try to not use AI, use simpler solutions. Of course, I felt a lot before in my career, I felt a lot in the trap of trying to over complicate things until I learned out that the most important things to generate impact, to drive impact, to move pointers, KPIs. And I was able to focus on that. And in the interviews, when asked about technicalities, I always started reaffirming, reassuring that the most important thing is the pointers were trying to move the impact we want to generate. In the case AI is necessary, then I would work like that. And I think it was important for a big organization like that not to have someone that is blind, blind by the excessive brightness of technology. I think this is the key phrase, don't let yourself blind, get blinded by the excessive brightness of technology. Awesome, awesome. You actually share this with our alumni panel for our private communities inside PMA. Regarding now we have AI, you're selecting the best AI for a manager to join the company. You set up a trick question, right? Can you set up, can you tell us the trick questions? You set up and show them, as an example, what do you mean by, hey, AI is the last solution, but lots of people just wanna dive into AI and show them, I noticed AI and everybody has AI. Everyone's every startup's company, they say, oh, where AI start up, they can raise more funding. Everyone just wanna add AI to it, but sometimes it's not necessary. So show us the example, show us within a private conversation and take the best AI candidate. Yeah, okay, so at that time, I was a lot of the time in the side of the person who hires someone. And even for data scientists, at that time I was hiring a data scientist. I don't go all the way only, I don't look only to their ability with algorithms and their understanding on that. So I had a trick to see if the person is actually thinking or is just repeating things. So one tricky question I came up is, actually it's a three-part question. First I ask, if you had an exam and you had to use to detect, for instance, a nodule in this image, what technique would you use? Then the person answers convolutional neural networks, okay, cool, so this is the best result, great. If you need, then I went to the second question. What, if you need to detect horns in a sound, what technique would you use? Then the person answers, recurrent neural networks. Great, okay, that works. And then I came with the final killing question with 90% of people failed directly. Okay, so now which technique or solution would you use to control the traffic lights in a crossing? Then the person answers, ah, columnar AI. No, man, no, we prove it instantly because it's statistical, it's an statistical tool. It will fail no matter what. 1% of the time, 5% of the time people will die. So you'll never use AI for a critical application like that. And it was very important because I was working in the health tech sector. So you don't have room for mistakes. You need to know when can you be fast, when can you use AI, and when do you need to go slow to be very, very careful with what you're doing. That's the trick, yeah. Very smart question. So basically you're like, all other questions about true AI questions. Last question, you shouldn't use AI for traffic lights control at all, or even flight control. I have some student come up, oh, why don't we use AI to control the flight pattern, like Delta flight, United flight, use AI to control the flight of the airplane. I said, oh, then we're gonna have airplane crashes soon. Sooner or later, or whatever small percentage chance will happen because AI sometimes, as you said, is a mathematical algorithm. Sometimes it may make mistakes, but when it makes a mistake or mission-critical things such as flight, or healthcare, or even traffic lights, it cannot happen at all. It's not horrible. So people just want to step in AI as a keyword to show them they know something, but they will just trick by your smart questions. That's very smart. 90% of people fell on the spot use AI to control traffic lights. Cool, I learned something new. Everybody, if you like this new interview, trick questions, then comment down below and like this video. Let us know what do you think. All right, so Nick? I have a lot of them. You have a lot of them. Comment below. If you want to hear more, we want to make more video about tricks. Massfell, AI interview questions, 95% of them failed. What about you? Awesome. Okay, cool. So now, I'd like to talk more regarding what specific most important shift that pushed you to the next level? For example, as you jumped from just near PM to kind of AI in a large international company, what do you think is the most important shift that pushed you to the next level? I think the most important shift was to, there were two that I can see looking retroactively right now. The first one is that I started to understood my career as a product. I always said I am not a good salesperson. I'm not a good negotiator. I am a good product manager. So this, how to manage a product, I know how to do to look at the KPIs to troubleshoot what's happening, why interview, get feedback. When I just something click in my mind, I think maybe one tip or trick that someone gave me in the community, then I started to understand, oh, okay, so if my career is a product or this journey of getting a better opportunity is a product, I need to understand my funnel. Where am I failing in, or where am I losing the opportunity in the funnel? What's the conversion rates expected from sending resumes to get interviews, to getting a second interview, to getting to the hiring manager, and then getting offers? And I selected KPIs and used the linkage to understand how many people were visiting my profile, how many recruiters a week. So I use these techniques and was able to get to the bottlenecks and then went from two months without any interview to two, three interviews a week. So it were small things. It's not worth that I say it here because for every person it's going to be different. But what is important is to understand where are these real knots, little knots that are preventing you. Another knot that was preventing me from success is cultural traits that we Brazilians have. So it's for us, it's very arrogant to say, I have done this, I have done that. So even for our interviews in Brazil with Brazilians, if the person just starts talking, I did that, I did X, Y and Z. We think, well, what about the others, the team? So we directly understand that this person is not a good team player. And for us Brazilians collaboration is extremely important. So, but for US I learned that it's different. So I needed to say, I did that because I did that and it's not, there's no shame on that. So that's helping me a lot. I think because for a new US company or others, if you just say we did that, they think different. They think that probably you are only one more person in a big team and probably didn't do nothing for real. Yeah, exactly. I like you highlight those culture differences. Actually in the US, you're right, it's opposite. When you're going to interview, you say, we did this. They didn't know what exactly you did.  And US culture is also very big on entrepreneurship and leadership and leader always. I led a team to do this. Yes, a team of 20 people, but I led them to do this. All right, it's more about help you to stand up from the competition, but you're right. I'm Chinese culture as well, we try to be humble. And it doesn't work in other countries, but only work in US cultures is very, very special. I'm glad you brought this up. So people are more aware of the differences between different cultures and also know how to navigate when you jump from countries to countries. So Nicholas, you have started your head of AI position in a new job. So can you tell us, give us high level overview? What does the AIPM do in your day to day? So what an AIPM does is exactly what the APM does, plus understand or be prepared for the additional challenge that can come with AI. These additional challenges are, for instance, now you have to deal with the data. And so the data lifecycle, you need to deal with engineers over complicating things. I can say because I'm an engineer and I over complicated things in the past. So engineers will always want to use the latest technology, but as an AIPM, you need to know enough to ask the questions that simplify the problems that target people to what is the impact that you are trying to drive. And the AIPM must also understand how do you make sure that your developments in AI are going to reach production and generate the impact you want. It's hard because you don't know exactly sometimes the level of data literacy, the knowledge the final user is going to have regarding AI. So these are questions you need to ask yourself. What's the level of explainability that will be needed? For instance, just a label saying you'll have a disease or not, it will suffice if you are building an AI system for a specialist radiologist doctor. Probably yes, but for a technician, you will need to provide the heat maps and the point exactly what's happening. But then comes the crucial question after that. Okay, so your user, understand what the AI is saying. Is he in a position to do any kind of action that generates value for him or for his company or for your company? Maybe not. So this AI prediction, the solution maybe should not exist. So I think this is the hardest part to understand. You start with the problem, but after that you need to ask what action do I need from the user to be triggered with the help of AI, achieve a solution for this problem. I see, so sounds like it's less about hey, let's hire some smart PhDs from MIT to build AI model. And it's not about hey, let's hire some smart engineer from Stanford to train the AI model to collecting data. It's more your understanding to collect data or to make sure the data is clean. But harder question is once you bring it over to the end users, how do they use it? And also is this the right interaction interface for them to use it? And also how exactly they can make decisions using AI, am I right? Yes, and you need to take it to have in mind that there are three levels of AI implementation. The first one is when you use AI to improve an existing feature or empower that. The second level which is deeper is when you build a new feature that wouldn't be possible without AI. And the third level is when you build an entire product that wouldn't be possible with AI. So AI PMs are most needed here in the third. If you are in the first level just improving an already existing feature that already generates value, probably you just need a data scientist or maybe a developer using an off the shelf solution. But the AI PMs really shine when you use AI to produce something that's completely new. An example of that are, for instance, self-driving cars. Sure. Something like that, yeah. Exactly, actually, we've talked several self-driving car live cases instead of PM accelerator talking about how AI is being happy to use in self-driving cars. Actually, I happened to build the very similar technology called machine vision in my first PM job as AI product manager and also the same technology also used for self-driving cars because the original one was more for smart cities to reduce car crashes, but the same thing being used by self-driving cars. You're absolutely right. This is a foundation for self-driving cars even exist running on top of AI, but some applications, if the marginal improvement, you may not need the AI product manager, maybe just existing product manager hire some data engineers as something new to it, such as something called Notion AI Fund, very funny. Notion is just no taking tool there. It's a very, very good organization tool. They just call themselves Notion AI and then have some kind of AI in it and become an AI company, of course, good for them. So it helps them to go IPO, increase the stock price, but it's not the kind of what you described, the very necessary true value of AI product manager to create for any company. Thank you for your insight with that. This is awesome. Yeah. There is one other insight that I'd like to share. I think I could also summarize the whole of an AI PM as someone that can understand the data strategy, match this with the product strategy and generate a business mode, so MOAT. So a sustained advantage for the company over time and leverages the power of the data to the core of this mode. So you need to build cycles like this. The more data you have, the better is your product. The better is your product, more users you have, more data you have, more or better data as well. So if you can build the cycle in which the quality of your product will always improve with the amount of data you have, so this data will be private, it will be generated within, it's monitored that just generate this data, part of it is generated within the company product. So the company will have something that others don't have. So technology by itself is never a mode, is never a sustained advantage, but this technology together with the data align it with the product strategy, then you'll have something that truly shines. It's an advantage like the network effect. So you have the network effect with providers and users. You put data on that and then it's the same thing, looking through, solve through another list. Awesome, I like how you teach other people using very simple concept to understand the cycles and continues to grow in the bigger group of stronger AI ecosystem. This is amazing. Nicholas, let me ask you. Nicholas, now as head of generative AI, what kind of advice do you have for people who want to break into product management or who want to become an AI PM? First of all, what I learned and you will probably learn as well, should learn is that if you are someone that is a generalist and wants to build things and make sure that these things achieve people that get in production, generate value for the person, for your company. If you're someone that has this mindset, chances are that you are probably already a product manager, you just don't know that yet. I was really lost in my career for many years because I wanted to do everything, but there was no specific role for this do everything person that is the glue between teams and outside teams. And then I learned about the PM role and then I thought, oh, okay, that's it. So there is a role that I can follow people, I can get courses on that. The first thing is to acknowledge if you have these aspirations and if you like to work like that, then get some formal training first. I started with a professor, Alex Cole, from Virginia Darden Business School, which helped me to organize my thoughts a lot and learn a lot of new tools. And then a few years later, with your help, Dr. Nestli, I was able to understand how to wrap this, all of this and leverage this for getting better good opportunities in the market. First of all, understand that. If this is your career PM, then get some formal training and okay, now becoming an AI team, which is more specific. I would spend some time or invest some time understand learning about the off the shelf tools first. What are the tools, the things you can use off the shelf? For instance, detecting things on images, detecting sounds in OCR, a lot of these things you have a lot of solutions within Google, Azure, AWS, you won't need to spend too much to use that do some proof of concept, maybe if you know how to code. And it's important, I think, to look, I think that data science won't be looking. How does the pricing strategy or rules work for the solutions? I found out a lot of solutions that look very cheap at the beginning, and then when you put in production that the cost scale very fast, the same things happening with generative AI already. I saw some companies with some pricing strategies. For instance, they charge very cheap for input tokens, but they are very expensive for output tokens. And they are a company that deliver proposals, for instance, so they generate a lot of tax. So it gets expensive very, very fast. So understand pricing. One experience I had was that at some point our company was stuck because the AI models were too expensive. We used it, the best tools available, that the big tax that was accelerating us suggested and the great work it, but then we were not able to launch more models because it was too expensive. We were with one million exams a year with a cost of $200 per model per month. Then we migrated to another tool that doesn't make the AI model online all the time, just scales up and scales down. And they were able to reduce this from $200 to less than $2. So because our peak of usage was very, very narrow. So I think if you start to develop these skills, understand the pricing of the off-shelf solutions, organize that on our mind, ask the question. I think as a PM you are already transitioning to an AI PM role. This is the very beginning. And then you get deeper with your engineers, you learn from them and then become even better. Another important thing is to start to get to understand, but I don't think this is trainable or you can study. You'll get with experience, the know-how, the feeling, when to pursue an in-house solution, when to hire a solution from a provider, a small startup provider, and when to wait for a big tech to launch the same solution. I see. Basically, there's a steps of tiers people need to do, right? You must understand the basic product management be a great product manager first, and then you can make AI specific decision in terms of pricing, existing tools, which wants to use basically usability of customers. And then you figure out how would you try to like in-house and outsource what kind of more high-level strategy decisions you need to make as AI PM. Awesome, this is great, cool. Hey guys, you know that Nicholas is going to film a second video talk about index regarding general AI for everyone training for everybody. So make sure to like and subscribe this video and also check out our next video right here that is the general AI training for everybody. So you can also become AI product manager. You're going to dive into all those three steps we just described here. Looking forward to seeing you in our next video. And thank you so much, Nicholas, for joining us today. If people want to find you, where can they find you? Okay, so LinkedIn, I don't have any other, I already have Medium as well. So sometimes I post these thoughts, these insights I have there. So Medium and LinkedIn, I think is the most. Awesome, yeah, reach out to him on Medium and LinkedIn. I'm going to share all the links in the description of this video as well. Thank you so much for joining us, Nicholas. We're going to see you in our next video right here. We've got an in-depth training of generative AI for everybody. This is darknessV from PMXCenter.io. We'll see you in my next video right here.


from theCUBE studios in Palo Alto and Boston. It's theCUBE, covering IBM Think. Brought to you by IBM. We're back. This is theCUBE's coverage of the IBM Think 2020 digital experience. My name is Dave Vellante. This is wall-to-wall coverage over the multi-day event. Harish Grama is here. He's the general manager of the IBM public cloud. Harish, welcome back to theCUBE. Good to see you. I'm sorry we're not face-to-face, but this'll do. Yeah, thank you. Great to see you again as well.  What do you do? You know, I want to start by asking you, you did a stint at a large bank. I'd love to talk to you about that, but I want to stay focused. You said last year on theCUBE, you can't do everything in the public cloud. Certain things need to remain on-prem. I'm interested in how your experience at the large financial institution and your experience generally working with, you know, your colleagues in the banking industry. How that shaped your vision of the IBM public cloud? Yeah, I think that's a great question. You know, if you think about trying to transform yourself to public cloud, a lot of people, what they try to do is, you know, they take applications that have been running their enterprise and they try to redo them in its entirety with microservices, using has-level services and trying to put it all up in the public cloud. Now, you know, just think about some of these applications that are running your large institution, right? Some of them will have regulatory rules around it. Some of them have latency requirements or low latency requirements, I should say. Some of them need to be close to the backend because that's where the data is. So for all these reasons, you know, you have to think about a holistic cloud picture of which public cloud is, you know, integral to it, but some of the things will need to remain on-prem, right? So when I build my public cloud out for IBM, I kind of keep those in the back of my mind as I get the team to work on it to ensure that we have the right capabilities on the public cloud. And then whether it makes sense, you know, have the right capabilities on the hybrid side as well, working with my colleagues in IBM. Well, you know, the COVID-19 pandemic, we've been talking to a lot of CISOs and CIOs. We had a couple of roundtables with our data partner, ETR. And it was interesting, you know, organizations that maybe wouldn't have considered the cloud, certainly as aggressively, maybe they put some test dev in the cloud, but you know, have said, well, we're really reconsidering that. One CIO actually said, you know, I'd love to delete my data center, but to your point, you can't just delete the data center. First of all, you don't want to necessarily move stuff. Second of all, we've got a lot of experience from a consulting standpoint looking at this, if you have to migrate, migrate is like an evil word, especially with mission critical systems. If you have to freeze code and you can't upgrade, you know, for some number of months, you may be out of compliance or you're not remaining competitive. So you have to really be circumspect and thoughtful with regard to what you do move. I wonder if you could comment on that. No, I completely agree with what you're saying. You know, if you think about, to your point, right? With COVID, things have really changed. I've been speaking with a lot of cloud transformers, I would say, you know, in the various industries, but specifically with banks as well. And the cloud leader for one of the large European banks said to me, he said, this was amazing because for four years, he's been trying to get his CSOS organization and risk and compliance, et cetera, to get their heads around moving applications to the cloud. And he said that, you know, one month of COVID and having everyone locked down at home has been able to unblock more than what he's tried in the last four years. So that's telling in itself, right? So look, you know, I've been working on public clouds for a good long time, now both from a provider side, as well as a consumer side. And while, you know, you certainly just can't close your data centers that are running your large enterprise overnight, you certainly can take a lot of stuff over there and move it to the public cloud in a meaningful fashion where you're able to take the pieces that really iterate more rapidly where you can get the innovation while keeping your data safe and, you know, being able to connect back into your backend systems, which run a lot of your large processes in your enterprise as well. So I think there is a balance to be had here and people, especially banks, I would say, haven't been moving so much to the public cloud. And I think this is the time where they're starting to realize that there is a time and place for a bunch of applications that can safely move. And that gives them the agility and the productivity while everyone's locked at home. And I think that's the eye-opener here. So I'd love to have a frank conversation about why the IBM cloud.  People put them sort of in a category of hyperscalers. Great, fair enough. And people oftentimes dismiss, you know, the IBM public cloud, however, your point that you just made is critical. And Ginny Rometti, who was the first to kind of make this point, Arvind's picked up on it that 80% of the workloads still are on-prem. And it's that hard to move stuff that hasn't moved. So, and that's kind of IBM's wheelhouse.  So what's the case for the IBM cloud? Why the IBM cloud? And why even move that stuff? Why not just leave it where it is? Yeah, so I think there's a couple of answers here, right? One of them is the fact that when you talk to the hyperscalers, and by the way, I can't stress enough, we're a hyperscaler as well, right? People have taken a look at our cloud from about two plus years ago, which point in time we were not, but we certainly are, and we can provision VSI's and so on and so forth as best as the best guys can. So I want to just get that out of the way. But to your point, the reason why you would consider the IBM public cloud is when you talk to the other people, they come at it from a very narrow perspective, right? They think about, you know, use VSI's on x86 using cloud native pass services. Now, you know, I want to stress again that we do all three of those things extremely well, but if you think about how large enterprises work, nothing is as clean as that. I did say there was a lot of applications that have been running your institution that you can't just willingly rewrite. And then you have bare metal, you'll have power systems, whether it's AIX or I, you'll have some Z in there, Z-Linux in there.  So we really meet our customers where they are in their journey and we give them a wide variety of capabilities and choices and flexibility to do their applications on the public cloud. And that's what we mean by saying our cloud is enterprise ready, as opposed to the narrow answer of you'll do everything with VSI's x86 and pass services. Yeah, I like that. And I want to circle back on that. Thank you for clarifying that point about hyperscalers. Having said that, I've often said, and I wonder if you could confirm or deny it, it's not IBM strategy to go head to head on cost per bit. Even though you will, you'll price it very competitively, but your game is to add value in other ways through your very large software portfolio, through AI, things like blockchain and differentiable services that you can layer on top. I've often made the point, I think a lot of people don't understand that, that insulates IBM from a race to the bottom with the, I'll call them traditional cloud suppliers. I wonder if you could comment. Yeah, you know, so I have to stress the point that just because I talk about all our other distinguishing capabilities, that people don't walk away with the impression that we don't do what any of the other large cloud service providers do. You know, to your point, we have AI, we have IoT. We've got 190 API driven cloud native pass services where you can write a cloud native application just like you put on the hyperscale, other hyperscalers as well, right? So we give nothing away. But for us, the true value proposition here is to give you all of those capabilities in a very secure environment, whether it is the fact that we are the only cloud where we don't have access to your data or your code because we have a keep your own key mechanism where we as a cloud service provider have no access to your key. Nobody else can say that. So it is those enterprise qualities of service and security that we bring to the table and the other architectures and the other, you know, constructs around bare metal and containers, et cetera, that distinguishes us further, right? So that's how it really, so these are really important points that you're making. And I know I'm kind of bringing out probably parts of the landscape that IBM generally doesn't want to talk about. But I think it's important, again, to have that prank conversation because I think a lot of people misunderstand. IBM is in the cloud game, not only in the cloud game to your point, but has very competitive, you know, from an infrastructure standpoint, so many companies in the last decade, we saw HP try to get in, they exited very quickly. Joe Tucci, the CEO of EMC said, we will be in the cloud and they ended up buying Mosey and exiting that. So Dell right now, you know, and won't have a cloud play. VMware tried to get in and now is, of course, big partner of yours. So you got in. And that to me is critical just in terms of positioning for the next decade and beyond. And the other piece of differentiation that I want to drill into is the financial services cloud. So what is that? You obviously have a strong background there. Let's dig into that a little bit. Yeah, if you look at the way most banks or actually every bank uses a public cloud is they build God rails, right? They build God rails from where their data center ends to where the public cloud begins. But once you get into the public cloud, then it really depends on the security that the cloud service providers provide. And the CSPs will tell you that they have a lot of secure mechanisms there. But if you ever speak with a bank, they will never put their highly confidential data-bearing apps with PII on a public cloud because they don't feel that the security that the cloud service providers provide is good enough for them to be able to put it there safely, number one. And number two, prove to their regulators that they are in fact in compliance. So what we've done is we worked with a Bank of America and now a whole bunch of other banks that I'm not allowed to mention by name as yet. Where we're building a series of controls, right? These are both controls during your DevSec Ops cycle when you're building your app and another 400 plus controls in the runtime that allow you as the bank to securely take your apps that have highly confidential data and PII and put it on the public cloud and we'll give you the right things whether it's the isolation of the control plane and the data plane or it's the data loss prevention mechanisms, the right auditing points, the right logging points, the right monitoring points, the right reporting data sovereignty. So we have controls built into the cloud that enable you to do all of this. Now, banks will be quick to tell you that the onus of proof is on them alone to the regulators and we can claim that for them and they're absolutely right. But today they spend hundreds of millions of dollars collecting all of that and providing that proof to the regulators. You use our cloud, we automate a whole bunch of that. So you're not number one as a bank trying to implement these controls on a public cloud because that's not your job, that's not your core expertise. And number two, when you actually build these compliance reports, you're not spending millions and millions of dollars trying to put it together where the compliance regulator will say, yes, this is okay. We automate a large part of that for you. And I think that's the key issue we're solving here. I want to follow up and just make sure I understand it because when I talk to executives in the financial services industry and other industries, they'll say things like, look, it's not that the cloud security is bad, it's just that I can't map the edicts of my organization into it certainly easily or even at all because I'm getting a sort of standard set of capabilities and it may not fit with what I need. What I'm hearing is that IBM, you guys are enterprise, you're used to specials, but so that's part of it. But you also said, they feel sometimes the cloud security is not good enough and I want to understand what that is specifically if IBM is doing something differently. So two things there. One is your willingness, whether it's auditability, transparency, mapping to corporate edicts and it may be other things that you're doing that make it better relative to good or not. Yeah, absolutely. So one of the things is, as I mentioned, it's the mechanisms like keep your own key which is fundamental to building some of these compliance safeguards in. But the fundamental different thing we've done here is we work with the Bank of America and we've defined these controls to use your language that maps to their edicts, right? Which should map to every bank's edicts now. There'll be a couple of extra controls here or there, but largely they're all regulated by the same regulators. So what satisfies one bank for the most part satisfies every other bank in the US as well, right? And so specifically what we've done is we've built those controls whether they're preventative controls or compensatory controls in the CI CD pipeline as well as in the runtime on the cloud. And that gives them a patch to automation to produce the right results and the right reports to their auditors. And that's really what we've helped them do. So I know I'm pushing you here a little bit. I'm going to keep pushing if that's okay. It was a great conversation. Yeah, absolutely. When IBM completed the acquisition of Red Hat, the marketing was all about cloud, cloud, cloud. And I came out and said, yeah, okay, fine. But really about his application modernization, that's the near term opportunity for IBM. You certainly saw that in the last earnings report where I think you're working with a hundred plus clients in terms of their application modernization. So I said, that is the way in which this thing becomes a creative, which by the way, it's already a creative and a cash flow standpoint. But I'm going to press you on the cloud piece. So talk about Red Hat and why it is cloud in terms of a cloud play. Yeah, so this is the power of Red Hat and the IBM public cloud. And of course Red Hat works with the other cloud service providers as well. So if you think about modernizing your application, the industry pretty much has standardized around containers as the best way to modernize our applications. And those containers are orchestrated by Kubernetes. That's the orchestrator that's basically won the battle. And Red Hat has OpenShift, which is an industry leading capability. It's a Kubernetes control plane that manages containers. And we from IBM, we've put our content, we've re-factor our content into containers and we've made it run on OpenShift. And we have a cloud managed OpenShift service on the IBM public cloud, as well as an on-prem that really helps bring our content to people who are trying to modernize their applications. Now think about an application that most people try to modernize. You know, rough rule of thumb about 20 to 25% of it is application code. That is the onus is on the client to go and modernize that. And they've chosen containers and Kubernetes. And the other 75% or 80% arguably is middleware that they've got, right? And we've re-architected and refactored that middleware into containers managed by OpenShift. And we've done 80% of the work for them.  And you can run that on-prem, you can run it on the IBM public cloud and I give you a cloud managed OpenShift service to do that effectively on our cloud. So that's how- That's interesting. Yeah, that's very interesting. I think there are, you know, probably at least three sort of foundational platforms. One is obviously Z mainframe. It's still much of IBM's customer base, you know, tied to the Z and it drives all kinds of other software and so forth. The second is middleware, to your point. And you're saying you refactored that. I think the third really is your hybrid cloud strategy. You kind of made the point, you threw an on-prem. It's, to me, it's that end-to-end piece that's your opportunity and your challenge. If you can show people that, look, we've got this cloud-like experience from cloud all the way to on-prem, multi-clouds, that is a winning strategy. It's jump ball right now. Nobody really owns that space. And I think IBM's intent is to try to go after that. I think you called it a trillion dollar market opportunity. And it's obviously growing. Yes, that's exactly right. And the peace parts that I've been describing to you, the way people modernize their applications all fit very nicely into that. Now, if you speak with the analysts, they're going towards a whole different category called distributed cloud, which basically means, how do you bring these capabilities that run on your public cloud to on-prem and to other people's clouds? And what I hinted at here is that's exactly where we're going with our set of capabilities. And that is a technical journey. I mean, Kubernetes is necessary, but insufficient condition to have that sort of nirvana of this distributed, massive distributed system, bring in edge systems as well. So this is at least a multi-year, maybe even a decade long journey. There's a lot of work to be done there. What would you say are there strategic comparatives for IBM cloud over the next several years? So I think for us, really it is building on this notion of the distributed cloud as I talked about. It is fully building out the FSS cloud, most of which we've already done. And some of these things will never be at end of job because regulations keep changing and they keep adding to it. And so you have to keep adding to it as well. So a focus on FSS to begin with, but then also to other industries as well, right? Because there are other regulated industries here that can benefit from the same kind of automation that we're doing for FSS. So we'll certainly do that. And we're in a good position because it's not only our technology, but it's our services practice. It's a pre-monetary that deals with regulators, et cetera. So we have the whole package. So we want to continue to build out on that, branch into other industry verticals, using our industry expertise across the board, services, product, everything. And then of course, you know, if there's one thing IBM has market permission for, it is understanding the enterprise and building a secure product. So we clearly want to evolve on that as well. Yeah, IBM has a lot of arrows in its quiver, including as we discussed cloud, now you just got to get her down as they say. So Harry, thanks so much for coming to the queue. Great discussion. I appreciate your transparency and stay well. Thank you, you too. Thank you so much. All right, you're welcome. And thank you for watching everybody. This is theCUBE's coverage of the IBM Think 2020 digital event experience. We'll be right back right after this short break.


Good morning from Thandi near Gibi Stated and stayed in this nice place last night. Normally I'm more of a budget traveler But we did share it with three people total PK and Pertique. Hello! Today we're gonna go explore the local mountain life and see some attractions in the area. So the roads here Well, I'll just show you. There are Nothing like I've ever seen before. Well, maybe Guatemala. I'm right now walking through the village of Thandi. It's near Gibi. And getting a little local information here. So right up on the hillside, this is where the locals live and then PK was telling me they own the land and they rent it out to For a year or for 10 years then city people can come and build a cottage kind of property like resorts and they can give Run like a resort and make good money. Okay. Here's a couple of local gentlemen. Namaste Kya hale? My name is Jack. Okay. Thank you. Yeah Yeah So people in these villages seem to be very friendly and nice. Yeah nicest people you will see in the mountains Uh-huh. Yeah, they are more tourist down there Yeah They are apple trees and they are building just with the wood like this is whole with made of Few stones at the base level and they're building with the only are they saving the grass for the animals? Yes, you're gonna get me home safe to mom Yeah, sure. So and that percent So these are some typical Typical Himalayan roads here. They're very narrow They get Washed out a lot from landslides snow Mud rain.  Yeah, the bus drivers don't like to be slowed down at all Okay, squeeze by GTA so we're in a place called mini Thailand. I'm not sure exactly why It is very beautiful. Hey, here we are In mini Thailand, I haven't been to Thailand, but it kind of looks like Thailand There's somebody peeking through though, let's go check out that spot. So wow so beautiful, huh? All right, look at that All right, so something super interesting just happened this gentleman just walked down here with this Giant basket backpack with all these little things and he set up some cushions It's just right where we're hanging out and he this is him. He's the The chaiwala the coffee walla Maggie walla and he has these local food service to do so I don't know what we're gonna try but I was very excited because I was we haven't eaten breakfast yet And also particularly is going in the water and so let's increase minus temperature minus Oh India Wow, they just grabbed the water Right out of the river He's got a little teapot over there and all the supplies and his food I'm guessing this is fresh milk cow milk. So cow milk without butter cow milk without butter. Uh-huh Here here morning milk He's checking for quality making sure it's good stuff before he Forges us a few chai Try by the river. I think is the name of the school Or maybe since we're at Thailand high child On your button, okay? Okay, cheers. Yeah, this this restaurant is called Thai chai Yeah, yeah, okay, and again, this is a Sidhu and there's fruits and nuts on the inside. It's like momo's Nuts, yeah, nuts and fruits. Okay, so just and it's really good for winter Okay, you can take the sauce. Yeah, so straight this off this state take more sauce. Yeah, really good Okay, please brother. Here we go Yeah, that's a really good So little salty It's like bun So it's spongy steamed And it just has this really good buttery Salty Taste and I don't know what this stuff is What's what's the sauce here? We eat and the stuffing of dinner. This is green the same the momo's Material is same. Oh, it's same. The sauce is same on the inside sauce is made of green And then yeah, all these spices Wow, you like it The sauce is delicious. It's only in the mountain What a pleasant surprise I'm glad this guy showed up with his tea And his food Otherwise, we were just come down here to take some pictures check out the water His name is Ticam Name is Ticam, but people call him Ticam Seri because here in the mountain areas People know each other from the village name because the village are really small and the village is called Siri Yes, the village is seri. Hey Siri Yeah So people here in the Himalayas they can have more ganja in a day like marijuana But they take in little quantity so many times But it doesn't affect their body because they keep walking all the time and they do like Physical work more. So it's that's why it still looks really fit and he can do anything like he can Go up to the mountain. Yeah, more fit than us. Yeah, so so one stop shop for almost all of your needs here at Tai Chi


Let's get over to our man, Mr. Basil Chapman, and Basil, folks, okay, it's got a subscribe webinar coming up. It's going to be this coming Tuesday, March 19th, 4 to 5.30 Eastern Standard Time. Now, of course, Basil has a great newsletter, the opening call. Now, this is for his subscribers. But guess what, folks? You can be a subscriber very easy.  You can either go for a month, six months or a year. And what that's going to do is that's going to put you into the webinar this coming Tuesday. And Basil's going to be talking about the technical tools needed for the coming few months, okay? So when you get Basil's newsletter number one, you're going to get a great newsletter for the month. He has approximately 12 archives there that you can understand exactly how the Chapman Wave works. And then, of course, you're going to be into this workshop. And if you can't make the workshop live, remember that the workshop is also archived. Basil Chapman, what's going on? Hi, Tom. How are you? I'm doing great, man. You're self? Very good. Thank you. So, we're looking at, hopefully, the timing is just right this coming Tuesday. I'd say it is. We're banging around at highs up here, so. A lot of things are happening. So, I thought it would be appropriate at this particular point, since I'm anticipating that there's a digestive phase unfolding, and that some of the stocks that have had just spectacular gains are probably going to take a breather, and that there are a lot of stocks that are starting to form a very nice space looking out for the summer that depends on which country you're in, but summer for us, which will be June, July, August. Yes. So, I thought that the technical tools that are really important, there's one in particular I'll do right now, because this has always been so important to me. I call it the technical tool of last resort. So, you look at this, here is, let me just move that away, here's the Dow chart. This is a data chart, same thing on the right, same data chart. On the one, I've got the candlesticks. So, candlesticks to me are also very important. I've got a bunch of candlesticks that I've kind of invented that are my own Chapmanwave Roman candle, a bunch of others, but most importantly, I'm looking at patterns as well. And you can see on the right is the same chart, except this is the closing price during the day. So, this is an intraday chart, and it shows you that there's, if you look at this visually, you can see there's an arch forming over here. Well, there was a little arch forming there, but that nine-print rooting average never went pink, it didn't go, it's green, so it's above the 40, it never went under it. And now we're starting to head towards this level where there's a really good chance that over the coming week, we will see a pink nine-period exponential moving average. And let me show you what's so important about this. And Basil, is that on the daily or the weekly? This is the daily. But usually I have daily weekly monthly. In this case, it's the daily. Okay, cool. I've added the on-balance volume, and this is the same thing, except without candlestick charts, it's the price, a closing price that keeps every day intraday, you can see it coming down. So, what's really interesting, yes, here's the same thing. And you just asked me, is that a daily or weekly, he has the daily on the left. So, in the chapter-weight methodology, it's a very easy process. You try to identify the lowest low bar, and then when the technical starts to confirm, it goes from a buy signal to a buy mode. And when the stochastic, that's this green, sorry, red and green line right here, these two lines, when that whole steady above 80%, that's good, but when it's steady above 90%, that's fabulous. But there's also a certain timeframe that I'll be talking about, that this loss, and then when it comes down, there's often a good chance that it's going to pull back quite sharply. So, that's, at this point, it's still looking very good, that's the slow stochastic. Here's the MACD, this is the weekly chart, this is the MACD, and you can see that the price of the MACD, most of the time, and I say most of the time in my show with the technical technicians this morning I was explaining, that there are times, for instance, look at the daily chart, here's the Dow, because what you look at, let's go to the weekly chart. You see the chart pattern, if you sort of half close your eyes, you can see that this pattern right here of the price of the Dow, the daily chart, matches very much the price of the MACD, even the stochastic, but even more so the MACD, but every once in a while there's a divergence where if you had to use the MACD alone, you'd be really fooled because if you have the MACD going lower and lower and lower, the price is going higher, and as I showed you before, that 914, I call it the 914, moving averages, was holding green, it's really close to turning down, and yet there's that arch formation that I was talking about. I'm going to talk about these techniques, here's the technique, this is the Chapman wave, inside track, repellent zone, look how many times the price, I'll show you this on the S&P as well, look at this, how many times it got into that little inside track, couldn't break above it, even now, look at that S&P, it just got repelled, repelled, repelled, so on the downside I've got an inside track, I call this inside track, propellent zone, on the downside it's inside track, sorry, repellent zone on the bottom, it's the propellent zone, so now the S&P, the 9 is still over the 14, so this hasn't given a real signal yet, I've got it on a short to 120 minute chart, but the S&P hasn't yet given the signal to even go to a sell signal, the Dow in fact has given that, so this is going to be very important because using just the one tool, I'm going to be showing many tools, look at the QQQ in the NDX 100, look at that, yes, let me show you, it's even better, you can see it much clearer here, look, there's the QQQ, both the charts, the dating charts, look what happened, look, there's the QQQ, look at that green, it is so close, much closer than it was back on the, remember that sudden sharp pullback on 21st of February, but this is different, now the 9-speed moving average is coming down, on balance coming down, if you look at this, just the 3 moving averages, yes the Dow, yes the 9, yes the 14, you can see it's really close to turning down, you know that since March the 6th, I think it's March the 6th, that green line has kept one in the trade, if you've been long, the QQQ, there was one moment we, you could have got it, you would have been taken out and then got right back in the next day, so once, just one day, since March, since November the 6th, yeah the trend is, you know I was talking yesterday, I mean that's how the CTA's, Basil, you know, including the guy about, you know, John Henry, all these guys, they're trend followers and anyone, particularly in the futures market right now, as a trend follower is like, this trend has been amazing man, right, yeah. It really has been and only that, every day you're looking amazement, I mean there are stocks that are stocks like SMCI and I've been talking about the round numbers. Yeah that's a 1200% this year. This is amazing, it's down $18 right now but look at this, the way it's gone up, actually it's very interesting because one of the things I'll be talking about amongst many others is the round numbers and if you go to SMCI, I can't even count, I've lost count of the number of round numbers and what normally happens is, when you see a round number, I often think it's because someone desperately wants whatever that's stock, right? Yeah. Just put it on, I want it. But it made a 1229, this is a super micro computer, it makes a 1229 round number high, same day as it makes a 12, 12.00 open, Atmos peak D and I'm sorry to pull back, look at this, this gray mass is all the number, the round numbers, even today it had a 1085 low, a round number low, to me something's not right and I think that these stocks are going to come back and these people are going to be holding the bag in this short term. Folks come on over to our website right now, sign up for Basil's News Zeta, it's a 30-day money back guarantee, you're going to be in a great workshop Tuesday the 19th. Basil have a great weekend, safe weekend, we look for a show Monday. Same to you, Tom. Stay right there folks, come right back.


Hello, my name is Yannir Quinn and I'm going to give this session alongside Francesco Romani. We're both engineers at Red Hat and today's session is going to be about how to enable low-latency workloads in Kubernetes clusters. So let's start. Now that Kubernetes has conquered the cloud, it's time for it to move on to other domains. Four and foremost, conquering cloud native network functions, the cornerstone of 5G deployments. To make that happen, we need to address an important limitation, ensuring low latency. The workload of running cloud native network functions, such as workloads designed for telcos, stores interesting challenges to overcome. It's very demanding, requiring fine-tuning of the node behavior to ensure low-latency processing of network packets, where special hardware and software are needed. Kubernetes container platform can be augmented and tuned to become a great platform for some demanding class of CNF workloads, and this talk will demonstrate how. We will describe the features available alongside upstream Kubernetes to provide real-time guarantees and to handle low latency and high-performance workloads. Let's have a look of a feature use case taken from the telco world. A cluster admin handling a deployment that should provide low latency, real-time, or DPDK purposes, will want to configure it without deep understanding of all the interactions of the kernel and the operating system components that are in the mix. You should be able to do so in an easy declarative manner that will encompass all the moving parts to achieve this goal, such as isolating workloads for interruptions, for gaining better latency results for telco company, and on the web scale and edge fronts. So how do we do it? And how can we guarantee performance and low latency? Let's have a look. The ecosystem that will provide a way to achieve this is built from these existing and further developed components. We will explore the kubelet settings, topology and CPU manager, the kernel settings on the node using tune D, and how they interact to deliver the workload guarantees. Kubernetes tunings that can be achieved harnessing and manipulating kubelet settings and tangled with CPU and device managers, as well as applying topology structures. And doing that, on the side, we can be using operators, and we'll be using operators to apply machine configurations to the operating system itself, including installation of real-time kernel and other configurations between the kernel and kubelet. We will be setting node level tunings relayed by tune D. This also will be done by an operator. At the top, we have an operator, performance add-on operator. We will see it later on. It will orchestrate everything to achieve optimized clusters for applications sensitive to CPU and network latency. If you're asking yourself what is an operator, so an operator is a Kubernetes controller specified to operating an application. It's a Kubernetes pattern that is extending the Kubernetes control plane with a custom controller and custom resource definition that add additional operational knowledge of an application. We will see some of the operators in actions, as I mentioned, combine to tailor a tuning suit for our cluster. To optimize Kubernetes clusters for applications sensitive to latency, installing a real-time kernel and configuring the operating system to run as a real-time system is one of the key aspects. Linux can be considered as the best choice for high performance computing due to the years of Linux kernel optimizations focused on delivering high average throughput for a vast number of different workloads. A real-time is not necessarily superior or better than a standard kernel. Instead, it meets different business or system requirements. It is an optimized kernel designed to maintain low latency, consistent response time and determinism. All resources managed by any Linux kernel, CPU time is a principal resource. Tuning a CPU towards better performance aspects should be according to scheduling algorithms that use priorities and real-time policies to control execution, favoring high priority tasks while interrupts and preemptions are enabled for a much wider range of time than on traditional kernel. Let's see how we reach and touch the nodes in our cluster to make them real stars using operators. With the user machine config operator, one of the operators we mentioned earlier on and we'll see following the slides, the machine config operator manages the operating system and keeps the cluster up-to-date and configured. Using this operator, platform administrators can configure an update system D, cryo or kubelet, kernel, network manager and more on the nodes. To do so, the machine config operator creates a statically rendered machine config file which includes the machine configs for each node. It then applies that configuration to each node with the machine config server and the machine config controller relaying this configuration. In the following example, we see how a machine configuration can set a kernel type to real-time on a specific node in a declarative way. So we have this configuration called of kind machine config. We set real-time kernel as an argument and this will apply using the operator on the nodes. Let's see it in a micro demo now. Before the micro demo, we'll see another example here of a machine config capabilities which sets kubelets configuration such as CPU, memory and topology manager policies on the node. The configuration goes through the operator and finally lands on kubelet conf files for matching nodes. And now we'll get to the demo. So we'll see how we apply real-time kernel and kubelet arguments on nodes using the operator. First we'll see how we change the kernel nodes to real-time kernel. So here we label the node with an RT worker label. We create the machine config as we saw earlier on to set the node as real-time kernel. We create a machine config pool, a pool where the machine configurations we create will apply to. So if we apply the machine, the pool, all the machine configs associated with it will be applied on the nodes. According to the labels, the label we used earlier on was the worker RT. We get the nodes, we see the label and now digging into the node, we can see that the Linux has changed to real-time. That's one example. I'm using here OC but you can consider it as a CLI very similar to kubectl with additional capabilities. Next example, we'll see how we change kubelet configuration on desired nodes. So we create this machine config configuration with kubelet config settings that will sync into kubelet confiles on the node. So we have here CPU, system reserve values and kube reserves, for example, and also manager policy and recon sign period. We create the configuration, the pool already existed so changes has applied and then we go into the specific node we labeled and here we can see going to the kubelet confile that values has actually changed according to the configuration we added. So after seeing how we used machine configuration, let's talk about Tuned. Tuned is an open source system tuning service for Linux. It monitors connected devices using the Udev device manager. Tuned system settings according to a selected profile. It supports various types of configuration like syscatel, sysfs or kernel boot command line parameters which are integrated in a plugin architecture. Many of the plugins are supported by another operator, no tuning operator and include the plugins we see below, syscatel, CPU, bootloader for example. Tuned also supports hot plugin of devices and also stores all its configurations cleanly in one place. Tuned profiles that manifest the required tunings we desire can be defined hierarchically as we can see in the diagram which reduces duplication and simplifies maintenance. Tuned also supports full rollback where the system can be easily returned to the state before the profile was applied and it also includes a number of predefined profiles for common use cases. For example presets for high throughput, low latency or power saving are distributed. Ultimately we will want to ship a profile that best suits the tuning requirements we have. The profile can be composed from one or more presets to have our desired settings but more parts are needed to compile to complete the picture as we will see soon. Here we meet yet another operator after the machine config operator and no tuning operator. It is designed for node tunings that provide us a centralized way to customize node level tuning for cluster administrators, abstract OS version level dependent tunings details away, enable modularity and tuned profile inheritance, provide same defaults for control plane and worker nodes and also provide dynamic tuning with rollback without the need for node reboots. The node tuning operator manages tune D as a containerized Kubernetes demon set. It ensures custom tuning specification is passed to all containerized tune D demons running in the cluster in the format that the demons understand. The demons run on all nodes in the cluster one per node. Deployment of custom tunings is done by creating a tune DCR which includes profile data, recommendation logic and machine configuration labels and selectors to apply these tunings on desired nodes. So let's see a short demo of how the node tuning operator applies our tune D profiles. First we label the node we want changes to apply to as we did before. So in our case we see the label here. We create the pool to be associated with the nodes as we mentioned earlier on. And here we see a tune D declarative CR associated with the node tuning operator where we set several variables such as kernel arguments for isolated cores. But we also see it includes a real time and open chief nodes parent profiles in our example. So let's see the parent profile. The parent profile also sets several variables. For example under syscater the kernel hung task time of six to six hundred. So we get into the nodes again and we debug our specific node. We can see the kernel arguments has changed to isolated CPUs equals one. And also check the syscater value for kernel hung task time of six which is indeed 600. Let's look at the cubelet and let's see how we can tune it and then the work nodes for telco workloads. Let's start with the most basic computer resources the CPU cores. To meet the latency requirements we need to mitigate CPU contention and context reaches. To do so we can use the CPU manager which is built in in the cubelet. The CPU manager supports different policies. The static policy allows exclusive allocation of CPUs to the workload. Exclusive allocations allows us to avoid the context reaches and to reduce cache misses because no other workload can run on the exclusively allocated CPUs. However system activities can still run on isolated CPUs. For full isolation we need other settings like for example handling the error queue processing on housekeeping CPUs. Housekeeping CPUs can be selected using for example other cubelet options like the reserved CPUs option, command line option. The telco workloads very often require access to SRV virtual functions or to hardware devices in general. Kubernetes provides a device plug-in framework which works together with the device manager another component built-in in the cubelet and this system can be used to expose other resources to the cubelet. Hardware supported by the device plugins includes the aforementioned SRV virtual functions but also other devices like GPUs or hardware accelerators in general. The device plugins report topology information for the other the manage including the new affinity. The topology manager is another component built-in in the cubelet. It orchestrates the other resource manager like for example CPU and device manager. It allows to align the resources that the workload requires using the topology information provided by those managers. For example we can do NUMA cell alignment. The topology manager is comfortable using policies. With the most restrictive policy enabled called single NUMA node all the resources a workload require must be allocated with the same NUMA affinity. If this requirement is not met under this strict policy the workload won't be admitted. Other policies allow less strict behavior. The topology manager can do for example best effort allocation. If it's not possible to allocate the required resources with the same NUMA affinity the workload will still be admitted. Let's see a few examples of NUMA aligned and NUMA unaligned resource allocation in order to see how the topology manager works. In general we should not expect the NUMA cell to be symmetric or we should not expect the NUMA cells to all have CPU cores memory and PCI buses because in general NUMA cells may have different combination of those resources. For simplicity sake however we will consider a system with two symmetric NUMA cells each of them will have CPU cores local memory and a PCI bus attached to them. This is the simplest scenario and we use it for illustration purposes. Our first example is a NUMA unaware resource allocation. The workload is allocated regardless and NUMA cell alignment for the required resources. So those required resources required by the workload may span across different NUMA cells. This still allows the workload to run but the performance is not optimal because for example of the of the traffic required between the NUMA cells. For example if our workload gets core number three and the servivirtal function for NUMA cell zero on the left and core number six and the memory from NUMA cell number one picture on the right this is a legal allocate a NUMA unaware allocation does not prevent the workload to run but if for example core number six on the right needs to access the servivirtal function on the NUMA cell number zero on the left it must pass across the cross NUMA link and this causes a performance penalty. This is our second example as it is a NUMA aware resource allocation we can do we can obtain this allocation for example using the single NUMA node topology manager policy. In this case all the resources the workload requires are aligned on the same NUMA cell in this case the NUMA cell zero on the left. The performance is optimal because no cross NUMA link traffic is traffic is ever required. Let's see a demo of how the topology manager works on a real Kubernetes cluster okay we are now seeing the demo so let's run a workload which requires aligned resources. For workload perspective the only requirement is to obtain in the guaranteed class and quality of service class so this is the example workload which we are going to run which is very very similar to the workload we seen previously in the in the example slide so two cores are required and one servivirtal function so from the user perspective with the topology manager nablet there is no difference in the flow we just run the pod and it is running okay yeah it is running so which is more interesting is how the resource looks like from inside the pod so to inspect the resources from inside the pod we can run at the bug workload but first let's see how the definition workload definition actually looks like so again we require the same resources and we just run a different command because we want to run our debug tool so we create the pod as usual we wait for it to be running and here it is no not yet here it is so now we can jump inside the pod and check the resource allocation okay here it is so two CPUs core two CPU cores are required and this workload got two of them and both of them are bound to numal cell one we required one servivirtal function which is as a PCI device is again bound to numal on one so everything is aligned on numal not one we have seen so far how many moving parts like operator components cubelet settings we need to configure to ensure optimal performance for low latency workloads let's see how we can bridge together everything since this is also a quite complex task let's see how we can it could be automated and to automate it we can use for example another operator which is the performance operator to configure a cluster we can use this operator which simplifies the process exposing a high-level profile which allows us to configure the performance behavior of the cluster we use another higher level of abstraction the performance operator will orchestrate the configuration of all the components like the operator we seen the cubelet settings and it will drive them instead of user so the performance operator acts as orchestrator for all the cluster components so in this slide we see an example of all of how the performance operator will drive all the other components and for example some settings are still done almost directly by the operator itself like for example for now huge pages configuration this is how the performance profile could look like we for example we can request like huge pages we can select the reserved housekeeping CPUs and we can add command line kernel command line arguments and we will see now how the performance operator works in practice in a cluster okay we have a cluster with a few nodes a few worker nodes and we selected the role to make sure the performance operator can work can change the compression of worker node so we see the performance operator is running and we have how the spec looks like so again some kernel arguments some huge pages for illustration purposes the single number node couple g manager policy and in this case we also we are required the real time kernel and for that we need cluster capability to support real time kernel so for example we see again the huge pages and now we see how the performance operator works with the all the other cluster components for example we are now checking the tune d the tuning node tuning settings we seen previously in this session okay we are seeing how the tune d profile is translated in practice and now we're checking we are checking we're going to check the huge pages so this is how it should look like but now let's jump on a node and check how the oldest settings actually translated to node tunings for example again with proper clustered support we seen the RT kernel but we also seen the kernel arguments we required and the huge pages we requested in just a moment and here they are here it is huge pages configured so this is how so this is how the performance operator drives the configuration of all the cluster if you want to try the performance operator we can check the GitHub page of the project on which you find of course the source code the rating is open source documentation the installing instructions we have pre-built container image and very soon we'll have all the operator available on operatorapp.io to easier consumption so this concludes our session thank you very much for attending


Thank you for standing by. Good morning, folks. Welcome to Seesaw Theater's eighth annual inclusive theater festival in collaboration with Calling Up Justice. My name is Matthew McGrory, and I am this year's executive director of Seesaw Theater.  I'm wearing a gray sweater and blue pants and I'm standing behind a wide wooden podium that comes up to my waist. Hi, my name is Zoe Morrocco, and I'm Seesaw Theater's artistic director. I'm a five foot seven white female presenting person also with brown hair and glasses standing next to Matthew behind the podium. I am thrilled to welcome you all to this event and want to thank you for your support of all of our educational efforts to give you a bit of the history of Seesaw Theater. We originated as theater stands with autism, which was a special event hosted by another Northwestern student organization, the purple crayon players. It became an independent organization in 2014 and have expanded with our own mission and season of sensory friendly theater. We now reach students across the Evanston and Chicagoland areas through school workshops and our devised winter and spring performances. This year's inclusive theater festival would not be possible without the hard work, the dedication and brilliance of our conference and engagement director, Ashna Rai. She's been working tirelessly on creating an accessible and reaching an innovative event. Starting with Claudia Alec of Calling Up Justice, Ashna has brought this festival to a new level by elevating voices that need to be centered in these conversations about accessibility. We are beyond fortunate to have Ashna as a part of our Seesaw community. And I feel so fortunate to be the person to do this please join me in welcoming this year's conference and engagement director Ashna Rai. Oh yeah. The high tech portion. Good morning everyone. Good morning and welcome to the eighth annual inclusive theater festival. My name is Ashna Rai, I use her pronouns. I'm a sophomore here at Northwestern and I am this year's conference and engagement director for Seesaw Theater. I am so grateful to have you all in this room with me today. I'm glad that we've learned about diversity inclusion and celebrating people for who they are are acknowledge to the Northwestern campus is a predominantly white institution built on stolen land. fires, the Ojibwe part of Watering and Odawa, as well as the Menomere, Miami and Ho-Chunk nations. It is also a site of trade, travel, gathering and healing from more than a dozen other native tribes and will stay home to over 100,000 tribal members in the state of Illinois. While the Land of Closure is a good beginning, we want to be really aware of the fact that that is exactly what it is, a first step. It must be paired with action. Your welcome packets for this event include a couple rings that I have found useful in my journey of understanding the significance of Native American and Indigenous communities in the modern United States, and I encourage you to keep looking and educating yourself. It's the least we can all do. For students at Northwestern, please reach out to the wonderful members of the Native American and Indigenous student alliance, Nesa, if you'd like to be more connected with their work. I wasn't actually able to attend last year's Inclusive Theatre Festival myself, so if this is your first ever ITF, I'm in the same boat. I'll probably attend many more together. Monica, who is here with us and has been the most wonderful, patient and supportive mentor to me in this process, was the director of ITF last year, and she gave me some pretty big shoes to fill. When I received the opportunity to be the director of this year's conference, I began thinking about what I wanted to bring to this role. How could I use my experiences, my personal relationship with the arts, and my passion for education and research to show what ITF looked like this year? The first thing that came to mind was diversity. As an international BIPOC student in the Northwestern Theatre Department, I don't happen to see stories or characters that reflect the identity crips that I am a part of. I wanted my mission to get a lineup of presenters that showed the intersections of disability and racial justice. And in the process, I have the ability to form connections with nine wonderful and brilliant artists who come from all over the United States and work in very different areas of theatre. Believe me, I'm the most excited to have the opportunity to learn from them over the next two days. This year, I was lucky enough to reach out to Claudia Alec of Calling of Justice as a potential presenter sometime around June. Claudia was kind enough to take me on as a mentee. Help her produce a conference that should explore the boundaries of what accessibility and justice could look like. Claudia is not from Chicago and is thus presenting tomorrow over Zoom. But I'm sure she's watching over the hard-run live stream right now. And I just want to express my genuine gratitude to her, particularly so much about what activism, advocacy, and kindness looks like in this field. I have learned so much about the world through our simple weekly one-hour planning meetings. I feel most connected to our art when I share it with others. Whether that's through teaching magic in a public school classroom in my hometown in Indra, or through an art film or chatting with other C-cell world members as the bold sets for our shows. Currently, such an important part of art and C-cell's work truly helps people feel like they have a place to belong, whether they're loved, respected, and desired just as they are. So this year, with ITF, we are celebrating. We are celebrating the innovation. All of our presenters are doing incredible things in this field. And we're celebrating how much patience and intelligence it takes to create something new. We're celebrating community. The fact that we're all in this room together and we're all united on the fact that we're really the arts more accessible to everyone, not just the people that traditionally suit the age-old American theater canon. And most importantly, we are celebrating education that we are gathered here to learn from those who know more and that we all hope to make our practice of the arts better. I hope that this year and always, we will continue to promote accessing the arts in a way that leaves no one behind. When is the time for a C-cell to refer to anyone with whom we have worked with or connected in the past? I know we have many, many and over friends of C-cell in our mischief today. And there's no server joy to know that you will all have the opportunity to connect with this community, which really would not be here without the support of you all and the people who come together each year to make this possible. Thank you truly for being here. All the presentations we did today will take place in this room, where one of the four presenters is zooming into this base this afternoon. I want to stress that participation in these presentations looks different for everyone. I encourage you to be as active as you would like to be, but remember that you are always welcome to take a break outside of the room or work down the hall and take a left to reach our quiet room, which is the Evans room. We also have some fan favorite C-cell props available at the back of the room during the conference for your enjoyment. Located both in this room and in the quiet space. Some presentations were called for some more interactive activities and discussions, while others are more of a traditional lecture format. If you have not heard anything at all throughout the conference this weekend, I encourage you to find me, Matthew, or Monica, or any other C-cell board member. We are all wearing super cool ITF ID cards. This year, everyone first enough to collaborate with two amazing organizations. Clarem of Justice, a collective that produces performances of justice online, on stage, and in real life, has acted as our co-producing and mentor organization and helped us make this entire conference more authentically accessible. The Hiram Theater Commons, a full and open platform for theater makers worldwide, is live streaming our entire conference to their website this weekend. We can now be more grateful for their support and guidance in giving us this kind of exposure and helping us connect to the larger theater community outside of Northwestern and Chicago. I encourage you to learn more about both of these organizations and the amazing work they are doing by scanning the QR codes and the posters around the room. On the lot of Hiram, you can see that the wonderful category machine is literally recording our conference. And it is also being live streamed through Zoom on my laptop. If you have any concerns or questions about this, please feel free to speak with any C-cell team member in this room. Now, the camera will be recording the presenters in amounts to you if you ask a question. Please let us know either before or after if you would prefer to not be on camera. I want to emphasize that this is not an issue at all and we are happy to either cut your section from the video or make sure that you aren't seen. And we are more than happy to accommodate any kind of request. The 2023 Inclusive Theater Festival would not be possible without so many brilliant and supportive people who have been the best cheerleaders, mentors and partners I could have ever asked for. Monica, who has been a friend, mentor and teacher all in one for almost six months now and has given us the best of my judgment and instincts. Matthew, who never stops telling me how proud he is of me and takes pictures of me with the idea of banner by the arch, is one of her constant support, encouragement and seesaw shoutouts. Claudia Alec, who has taught me so much about activism and justice and mentorship in the past three months. Laura and Elise, for toileting my endless requests and tiny text edits on our marketing web chat. Claudia, for lugging on all of the content that we're currently filming. And everybody on the seesaw bird, for inspiring them with their passion and helping them build an ITF that I am truly proud of. Thanks for creating art and showing it with others. Thank you for joining us and welcome again to the eighth annual Inclusive Theater Festival.


The Nike Training Club app has literally been my saving grace for my health and fitness during this entire pandemic. And in this video, I want to share with you guys exactly how I use it to stay lean, some of the features I liked and disliked, and honestly, what set it apart from the rest of the apps that I made a video on a little while ago. So let's get right to it. What's up, everybody? It's your boy, Noah. It's your first time here. I make videos on the best tech and tips to help improve your health and productivity. If this is something of interest to you, go ahead and hit that subscribe button. So how did the Nike Training Club app help me stay lean during quarantine? Well, at the beginning of the pandemic, I basically didn't work out for an entire month. The gym was closed, my motivation was low, and I was a hermit. I didn't want to leave my apartment. I wanted just to stay home, stay safe. And after a while, I saw that it was affecting my body. My weight was fluctuating. I was eating unhealthy, and my energy levels were low, and I knew I had to change around. So I started searching the app store for a really good fitness app that I could do at home that would be a nice kind of entry level into getting back into working out. And that's when I actually came across the Nike Training Club app. The first part of the app that really helped me stay lean was their programs. And what I really loved about their programs was that it was a full on system, basically. They had the video walkthroughs where they had a trainer who was working out with you, kind of leading you through the workout, giving you motivation and giving you a timer when you need to switch and when the workout would end. And then they also had articles for every single week where it went over different motivational tips and tricks and also recipes, anything that helps you continue to stay healthy during that entire week. And the program that I started off with was the Breakthrough Challenge, which was a four-week program, five workouts a week that really challenged your strength, your conditioning, your core, your mobility. And it started you off doing like really beginner sets and reps. And then it really increased each week until the end of the month where basically you were being gasped, but you were prepared for it. And that's what I really enjoyed was that I felt like I was getting back into working out and getting back into the shape. But I was able to do so at a gradual pace, which was really helpful for me. And again, I really felt like I was working all aspects of my body. So overall, as being healthy, I was working on my core, I was building more of my strength and I was working on my heart health. And I think that's super crucial when it comes to any type of workout program. And the second way this app helped me stay fit during this pandemic were the one-off workouts. So once I finished the Breakthrough Challenge program, I wanted to be able to do workouts and kind of pick and choose which ones work for me based off of the time that I had and based off of the specific goals that I wanted to work on. There is a variety of exercises and workouts that you can do. You can choose anything from endurance, strength, mobility, yoga. And then on top of that, you could choose what type of muscle group. Do you want to work out your abs? You want to work out your shoulders? Do you want to work out your lower body? Then you could filter by how much time you have available. So that's super clutch when it comes to the fact that you don't have enough time to work out. Now for what I liked and didn't like about this Nike Training Club app. For the things that I liked, I love the fact that the production quality of all the videos, of all the exercises and workouts were amazing. You know the apps that have basically just the pictures of the exercise? They have like two pictures side by side of like the movement. And I'm like, what am I supposed to do? You know, am I supposed to follow this? And I think that Nike did a great job of really showing you what type of workout and exercise that you need to do. They have a trainer there who is helping you to do the correct exercises to feel in the muscles that you're supposed to feel it in and then also regressing when you need it to. And again, I think that's super important for beginners who are just starting out and also people who are maybe intermediate or advanced and are finding ways to activate their muscles more than they did before. And the second thing I love more, how tough those workouts were. Even some of the beginner ones are tough, but it's it's still accessible because again, they give you the regressions for the exercises.  And again, you're watching the trainers do it. There's no cut scenes. I'm like, how are they doing this? These guys are really fed, but it also encourages you to keep working out and exercising so you can get to that level as well. And then what's cool is at the end of the workout, you can actually say where you did the workouts and the effort level. And I would say that on average, I put seven to 10 most of the time. The third thing I love was the connectivity with technology. Now, the Nike Training Club app worked seamlessly with my Apple Watch, which I think is super cool. So whenever I started a workout on my phone, it automatically popped into my Apple Watch and I could see how many calories I was burning, you know, where I was in the workout and then also what my heart rate was at. And the one time I was done, it put everything into my Apple Health app and it was a great integration. And I think, again, that's what makes wearables so great. And I think what will really help for the future of wearables is the connectivity of everything. And now to what I didn't like and I wouldn't really say what I didn't like, but what I wish there was more of. The first thing was, even though they had a great library of workouts and exercises, what I really wanted was a little bit more of an update. So I kept kind of finding the workouts that I really liked and I would stick to those few, but I wish they added more workouts to this list. And I guess I was a little spoiled with Peloton where they had daily workouts coming out all the time. And I know that could be a little aggressive, but even though it was weekly, if Nike was just putting out weekly great workouts at the level that they did, I think they would literally be perfect. And the second thing I wish they worked on was the actual recommendations. Imagine this, you finish your workout from the app. There's a notification on your phone that says, hey, great work. You burned 300 calories tomorrow. Let's try to hit 400. But then don't forget to have a protein shake at the end of the day because your calorie levels are low. That would be sick. Like it's like having again, having a health coach, a personal trainer on your wrist. And I think if Nike or whatever fitness company or health sports company can figure that out, I literally think it would be game changing. However, ultimately, I think the Nike Training Club app is basically there. And I think it's the best app compared to all the other apps that I tried. One, because of the quality to because of the connectivity with all the technology that you have and was great in terms of workouts. And I know a lot of people love the Peloton app. But for me, I've been using the training club app. And that is literally why I can still say that I'm still lean right now. And I still was able to maintain the muscle that I had. So definitely recommend you to download it. I'll put the link in below. And one of the biggest things a lot of people ask me is which apps are free? Everything in this Nike app is free. So go ahead, check it out, download it. It has been game changing for me. And yeah, I know you guys will enjoy it. I know you will benefit from it. So that basically wraps up today's video question of the day. Have you tried out the Nike Training Club app? If so, let me know what your favorite workout is in the comments below. I hope this video is extremely helpful. If it was, go ahead and can pal that like button. Subscribe if you haven't already and I'll catch you guys later. Embrace the hype.