Hey everybody, welcome to Monday morning. So glad to see you here This is going to be an awesome session because I'm not going to talk about technical stuff today I'm not going to talk about, you know, how you do things or building sales pages or emails or any of that stuff What I'm going to do today is I'm going to talk about how you can be successful No matter what you do no matter what you do and I'm going to give you four principles that you can apply So it doesn't matter what's happened in the past. That's not necessary to even think about that Matter of fact, let me put a couple of your comments up your own screen But I'm going to give you the four things I think you'll agree with me that at the end of this session today You are going to have everything you need to be successful at anything you want to do Now we're going to be talking about making money online and I'll tell you I had a bad day yesterday We didn't do a lot of sales and this may shock some of you, but we only sold $8,900 worth of survival food yesterday 8,900 that's $8,900 And to some of you you may say well Jay, that's a pretty good day. It's not to me now It's not to me now now that used to be a good month for me when I worked for somebody else If I could make $8,900 I would think oh my god, I'm chilling it But now it's not it's not a great day for us Okay, and it could be the Ukraine thing that's going on. It could be the war stuff. I don't know. I don't know but the bottom line is my my whole View of you know, and I'm putting some of the comments up on the screen and my whole view of what success is has changed And I've learned these four things these four things are the Building blocks of my entire business now. They are the things that have made me, you know What I think is pretty successful. I mean we do six figures some days now. That's over a hundred thousand dollars in a day And it's exciting to me, but you know what I do now is entirely different I don't I don't say well gee I need to make some money to pay the rent or you know pay my bills because it's all paid Everything's paid right Thomas Chapo says Lonnie cannot be starting any later than me when you say that Lonnie, what are you talking about? How old were you? I was 50 when I started my online business. I was 50 years old And it looks like Lonnie here says and I'm going to start with the steps in one second But I want to knock out some of these comments I tried Commission hero, but wasn't able to get the help I needed seems like all they wanted was the cost of the program Very disappointing that's probably true with a lot of programs that are out there Let's see Lonnie. Well, I haven't found my name on the obituary list yet Yeah, I put in there the only only time it's too late is when you're dead. Okay when you're dead It's definitely too late, but outside of that. It's not too late. It's not In time you will be able to tell more about The good versus the bad stuff JR here is just good. No BS Just a lot of great advice. Thank you so much very kind of you Thomas, but put in there When did you start? I really like getting your comments in here. Okay, so I'm gonna give you the first one Are you ready? You can write this down if you want.   How are you gonna do that? How does that work? That's the craziest thing is when people say well You've got to have this exact plan as to what works Well, let's just be asked because if that was true if there was this plan that you had you could hand this plan To everybody and everybody would be successful. Why are we always trying to plan stuff? Planning is probably the most crippling thing you can do to yourself when you begin now I'm not saying planning is bad, you know in the future But right now in the beginning when you're trying to make money online to sit down and to come up with this plan That's gonna make everything work is insanity. It doesn't exist It doesn't exist. I own let me tell you what I own right now. I own a survival food company We sell survival foods. We sell emergency foods. I own some courses. I own email earning system I own a digital cash academy. I own Ecom Road. I own numerous courses I'm not gonna send their list them all none of that was playing not a one of them now why were they not playing well because All of my training courses were teaching people how I did stuff in my food company I didn't have a food company in the beginning I didn't start with a food company. I never intended to sell survival food I started off selling cell phone cases. I just started and that is that is the key I just started.  It's the most crippling thing. I've ever seen anybody do is to sit there and plan Colonel Sanders 65 Learjet and dinner 48 Julia Childs TV show 50, right Right Paul says all these people started late So number one is don't plan because you can't You can't plan something that you haven't already done or that you have deep knowledge it And if you have deep knowledge, you probably wouldn't eat the plan anyhow, so quit the planning crap I'm so tired of hearing people say well I've got to get just the right plan once my plan is all set up. Then I'm gonna go ahead and start working on this No, just go ahead and start working people say, where do I start as they throw a rock wherever it lands? That's where you start just start. Okay, where did I start? Okay, so I want to make some money online So I went to eBay And I started looking around at what was selling and one of the things that sold more than anything else according to all the stats I could find in 2009 and I'm going back to Was cell phone cases that was a number one selling item on eBay So I said well, I'll sell some cell phone cases now. I didn't do well at it. I lost money I had a bunch of inventory left over but it didn't matter because I started I just started and if you just start Guess what you start learning things more doors open up more windows open up more possibilities open up Okay, if you just start but the problem is everybody's trying to plan and then start and it doesn't work that way It works like this. You just start start doing something There's too many things if I were to tell you everything that I do you would say well That's too many things to even know it's not you will learn them along the way You're gonna pick them up, you know gradually you're gonna pick up all these things It's so funny. Sometimes I'll have conversations with my wife and we both work in digital marketing And we both do pretty much the same kinds of stuff that we have different niches And if anybody else were to hear our conversation, you would know what we were talking about it would sound like insanity You would say what are those two people talking about? Let's see here. Let me see what we got Paul says JR Fisher eight years to overnight success. Yeah Well, actually, it's been what how many years? 2009 this is 2022 So we're going on 13 years now 13 years and I will tell you in the beginning And I'm I don't want to ruin some of my steps now I want to ruin some of my steps, but I started off selling cell phone cases. Okay, that was my first Attempt to make money online. It didn't do well. It didn't do well and if it doesn't do well Don't worry about it. Okay. Now the next thing I'll tell you this is still in number two when I say just start This is not unlike going to the gym. This is not unlike playing chess This is not unlike learning how to make a webpage and here's why why they're all similar is because you're going to have failures Okay, you're going to have things that don't work out Okay, so number two is going to be devise a plan once you've learned some things Okay, what you've learned some things once you've learned to do a couple things now I go to the gym. Okay, and I have missed days at the gym. Can you believe that? I've actually missed a day at the gym, but I didn't say oh, I've missed this day So I can never go to the gym again because I missed a day That's insanity if I were to say it that way and yet a lot of people do that a lot of people will try to make some Money online and they can't go buy a program or they'll do a thing and they go Oh that didn't work. So I guess I can't make money online. No that one thing didn't work No, I don't need to quit going to the gym. I just missed a day You've got to quit basing all of the future on a few little mistakes a few little failure stuff like that Then you can devise a plan. That's number two Okay, you're going to devise a plan on what you learn and here's the other part You're not going to want to hear for number two. You need to work every single day Every single day. Yes seven days a week 30 days out of the month.  You've got to work on the weekends. You've got to work at nights. This is what it takes People don't want to hear that people want to hear that they can work four hours a week and make millions of dollars That's BS guys. It's never happened. It's never gonna happen Get it out of your head if I can get your head straight in this one training session today And and you can understand what I'm trying to tell you Then of course, you're gonna do really really well and that that one thing that really kills people And I see it happen time and time and time again is that they'll miss a day at the gym Or they won't make money or one of the ads they ran. Okay, you know, or you know, whatever Was there some failure? Okay? Let's see. It's only too late if you quit. Yeah, you know Gosh, this is this is so close to my heart because I know I have a lot of failures guys If you had seen all the failures or had if you'd seen all the times I wondered if I could make payroll if you had seen all that you would say well gee He deserves to make some money now, you know, you would have it with me for me, right? It is hard Okay, and if you don't if you don't want to hear that it's hard Don't listen to this video because anything other than that is not a truth A truth is it's hard to do and it's hard for a reason because if it wasn't hard Everybody would be successful and there'd be no definition for success Success wouldn't be a thing that wouldn't be a word in our vocabulary If everybody could do it and the reason That the successful people can do it is because they can do the really hard Difficult boring mundane things and they can do it every single day and they can show up every day When things aren't going well when things aren't working out. They still show up. They still come Okay, I go to the gym sometimes and I'm 62 now and sometimes it hurts It hurts bad. I have a friend. I was talking to him the phone last night He's been my friend since I was 13 14 years old. I'm we're still friends He's an incredibly successful person multi multi millionaire. He lives in Memphis, Tennessee And if you don't know anything about Memphis, this is where we grew up Steve and I grew up in Memphis and Steve if you're listening shout out to Steve. I'm very proud of him But we grew up in a really bad neighborhood Okay, there were drugs and there was fighting and there was violence and that's what we grew up in Okay, and maybe that was what was helpful for me Maybe living in that environment helped me out because later on when somebody said something was difficult or tough or it hurts I was like, oh, no, that's nothing You have no idea. I mean when you walked outside your house there It was dangerous. Okay, and I was one of the people that was dangerous and he was too We were both dangerous people, but we've since grown and he says now I try to be kind to everybody There were the odds of me and him and another guy This train of ours named Pat the odds of any of us being successful We're almost zero Steve was a 10th grade dropout Pat was a 10th grade dropout I did get through high school, but I mean the odds of any of us doing well were zero I mean it was just zero So the next thing I want to tell you is don't hang on to all those things that were so terrible to for you in life They don't matter. They just don't matter. Okay, it's irrelevant Paul says I think it's important that you invested in training and kept current Annually you didn't rely on free information. Oh, no, I've paid I've paid so much money in training. It's craziness Thousands and thousands tens of thousands of dollars in training and I'll get to that in one second, too There's there's another point to that a successful person is the one that kept on trying.  No, he's not he's not tough in that ring He's not talented in that ring He was tough at five o'clock in the morning every day when he showed up at the gym and practiced That's when he was tough That's when he was putting in the work The fight the actual fight is easy because he did all the time in the gym and I'm trying to think of the saying I think it is something that those who go through pain in times of peace are the ones who will Shed the least blood on the battlefield at times of war. In other words, it's all preparation It's all doing these boring horrible things every single day that the average person just does not want to do They do not want to go through daily pain. I had a relative that told me the other day She said that, you know, well, I don't want to get up every day and I have to do things. I don't want to do That's exactly what you need to do to be successful Okay, we've gotten through two of these two of these things and I want to expand upon them So let me get to my third one here And just I want it once again, let me just reiterate in number two It was to devise a plan based on what you learn But please don't let a failure in a single day or a missed day at the gym Stop you from going forward. Please. I'll do that. Okay. And the next thing is number three Continue to learn Continue to learn. This is one of the biggest things that's helped me Because when I started out in 2009, I couldn't type and I didn't know how to send an email. Okay, that's where I was at That was my starting point So I was dumb. I didn't know anything, but it didn't bother me It didn't bother me because I knew I was of average intelligence and I could learn things You're probably the same way. The only thing that's preventing you from being wildly successful Is that daily grind that seven days a week daily grind? You know, I'm gonna hit that seven days a week thing and nobody wants to hear it But last night I was working Saturday. I was working. I didn't work all day. No, I didn't work all day I may have worked a couple hours some of those days But the point is I didn't let up because if you start taking breaks that break becomes longer and longer That break that was just Sunday turns into Saturday Sunday And then it turns into Friday night Saturday and Sunday and then it turns into well I don't need to do anything on Friday because I'm at my other job right now You can't do it guys pretty soon You're gonna drop it your and your reason you're gonna drop it is because you kept shorting the amount of effort to put into it And when you kept shorting the amount of effort you kept getting less and less results You kept getting more failures because you were shorting yourself So number three is continue to learn but keep in mind the reason you want to do this is because things change They change every day online. They really do new things come out New programs come out new ways of doing things new ways of doing ads You know all these things change. Okay, so you've got to continue to learn stuff If you continue to learn stuff, then you're gonna stay on top of this stuff. You're gonna be good Sorry, I got a phone that's going off over here in the background. I really Apologize that a price will be paid or be left behind. You need to realize that no one's going to hand it to you D. Excuse me, D-W-E-I-T He's always got a cool acronym. What do you got fall do whatever it takes? Yes, whatever it takes by the way I think I was in the sister city Chicago. Oh, okay, cool. Yeah a Memphis. Let me get back to Memphis real quick And then I'm gonna talk about this learning thing Memphis The statistics for last year if you think that you've got a rough city and this is the city I grew up in 332 murders in 2021 332 some say it's 334 the the actual statistic was 332 now you say well Chicago had like over 700 Yeah, but what is the population of Chicago? Let me see here. I want to look this up. So I have this right Population of Chicago Okay, let's see here 2.71 million in 2019 so we're gonna round it and say 3 million people they had 700 murders that's horrible. That's terrible, right? Memphis had 332 with only 500,000 people Hmm, that's pretty bad. Isn't it 27% of their population is below the poverty line I was that 27% that was me, okay? Stop with this, you know looking at all this negative stuff and I'm gonna get to that in a second Let me let me continue on with the training part.  I want you to get good at things, but I want you to be careful I want you to follow people who are doing what you want to do and are successful at it and are Continually doing it. Okay, that's what I want you to do. I don't want you to follow every yahoo that comes out with a course Okay, how long have they been doing it? How much money have they made? I've been doing this going on 13 years now 13 years my first year and a half was not successful So maybe we'll push that aside, but after that I did okay. I did okay I can pay my bills. I can go where I want when I want I can wear what I want I can drive what I want I can travel to any part of the world that I want I would not be Ukraine right now. I feel so sorry for those people man. That is such a messed up situation But I think that's what I consider success. I consider success when you can do what you want Epic instrumental music. Hello everyone. What did I miss? You'll have to go back to the beginning to get the rest of this But I think you're really gonna enjoy it So this learning thing how can you learn you can go to conventions you can listen to webinars You can buy courses and you know what? I don't think I've ever wasted money on a course ever ever And I bought courses that made me no money now How could that be because I learned what didn't work? Okay, so if I got a course And the material was crappy. I learned stuff from it. I got something out of that I learned that the things in the course didn't work or I learned that the instructor was crappy Or I learned that I would never buy anything from that person again. That's okay to learn those things Pulse as I look back on my old crew and like you some did succeed and others didn't I think you make that decisions and finally decide to leave and break the cycle of Mediocracy that's very true. That's very true And you know, I would say most of the people I grew up with either are dead or in jail or a wildly successful That's the only two directions. They really went kind of funny right matter of fact three out of four of my best friends Dropped out of high school three out of four of my best friends dropped out of high school Two of those three are wildly more successful than I okay hundreds of millions of dollars. Okay hundreds of millions I did okay. Okay. I've sold millions of dollars online. I've done okay But the point is none of us none of us Had our future decided upon by our past Okay, and I want to get into that and just just a little bit deeper into that in just a second But I do want to go over this this third step here in this third step is about learning now I've talked about learning learning is important.  I had somebody one day since I just bought your course And I'm really looking forward to being successful, and I said to I said buying my course is not going to make you successful I can guarantee it's not going to make you successful What you do with the material? Could make you very successful if you don't quit if you keep going to the gym Okay, and I use the gym as an example because most people have a really hard time with that one They can start going to the gym they can buy memberships they can try to work out But then then what happens they quit and when you quit you lose all the momentum. It's all gone Paul says the biggest thing to learn is to embrace change by the way We finally moved when we found a dead guy in a parking lot and guy got his knee cap in front of bad store Yeah, you know and Paul, you know where I grew up in Memphis. It's just not good and if you're a little rock That's pretty bad too So yeah So learning learning learning learning 5% of the time 5% of the time learning 95% implementing 95% implement. I will tell you I've learned a lot from training I've learned a lot more from implementing I learned a whole lot more when I try to do something and it works or it doesn't work because that's the only two ways It can go when you try something you see they're gonna work or it's not gonna work Okay, there's no in between okay, but I've learned a lot more from implementing than I ever did from training But you need training so it can send you in a direction to do something Okay, so that's our number three is that you have to continually learn you have to continually gather this information because it changes because there's new things because because because You just got to do it you got to do it. Okay, so and I've got a little bonus at the end too The next thing I want to tell you which is number four You're not gonna want to hear this. I tell you a lot of things you're not gonna want to hear Don't I it's kind of funny, but it's the truth Oh one thing I did not say to you which I should say to you is if you haven't subscribed to this channel Please do so. Please do me that favor Do that and ring this bell turn on all bell notifications So you're notified each and every single time I go live and give me a thumbs up if you would too I've got to promote my channel. I gotta do all that stuff Let's see By the way, how did KC course go very well very well Paul? I didn't stick that up on the screen So I've got my notes in front of me now But number four you ready for number four write this down Because this is holding a lot of you back. Let me get a sip here real quick This is the one thing that holds a lot of people back This is the one thing that they spend all their time working on and all it does is hold them back Are you ready for it? Here it is. You don't need a mentor No, and I gotta tell you guys every single week. I have several people approach me and say I want you to be my mentor Now, what does that mean exactly? What does that mean exactly? I think it means I Want you to do all the work and I want to just copy that and be successful in other words I don't want to do the work. I don't want to put in the effort I want to have some guy tell me how to do everything now. Do I mentor people? I have in the past of my choosing Okay, so I'll see somebody else. Hey, I think I can help you with that Let me let me give you a couple pointers.  Okay, and I'm not gonna take them under my wing and go let me make you successful Why should I do that? And besides that it often doesn't work. It just doesn't work It doesn't work because if somebody's looking for somebody else to give them all the information That means they're too lazy to work it out on their own You don't need a mentor Elon Musk did not have a mentor. He's the richest guy in the world Now, how are you gonna argue with that? How are you gonna need to tell me that you need a mentor when the richest guy in the world didn't need a mentor? How are you gonna do that? Mark Zuckerberg didn't have a mentor. You can love him or hate him. Whatever I don't especially like him, but he's successful and if you're looking for success You say well, did he have a mentor? No, he didn't have a mentor. Didn't have one. What about Albert Einstein? He's pretty smart guy, right? No mentor matter of fact I think you're gonna find that some of the most successful people in the world do not have mentors That gets in your way because it impede your free thinking it prevents you From learning more it prevents you from taking risk it prevent because there's some mentors Then they're not gonna think the way you do right and if that's the case Maybe there's another direction you need to go in and the mentor may prevent that you don't need a mentor You don't need one. It's okay to hire a coach if they really know what they're doing Coaching is an interesting thing. I did it for a while And I didn't like it and I got to I was charging about two thousand dollars an hour And I decided that that two thousand dollars was not enough for my hour now You may say well, that's arrogant or that's stuck up or my goodness JR That's terrible. The only way I'll mentor somebody now is for free I'll help somebody for free But I don't want to charge somebody because you can't pay me what it's worth in my time to work on my own business Understand when I say yes to one thing I say no to another and so do you so if I say yes I'm gonna mentor you that means I'm saying no, I'm not gonna work on my business Right, so why would I do that? Well just to be a nice guy JR Well, I am a nice guy and I do help people but I do it for free. I don't charge Okay, now my courses if you want to learn more grab one of my courses they can teach you a lot Let me see here. I'm gonna get back to some of these comments here real quick guys And then I've got a bonus thing at the end here Real bonus at the end. It's gonna help you out a lot By the way, okay. Yeah answered that when I just improved the screen. What personality traits and tactics Did your successful buds do that separated them from the hard the herd? I'm gonna say the number one tactic is they work harder than anybody else Pat ended up Working literally seven days a week 12-14 hours a day Steve still does that He takes off Sundays. He takes off Sundays always takes off Sundays But I'd be willing to bet he does some work on Sundays even though he doesn't admit to it So that's I mean hard work is it?  He says that's a good question But that is it hard work and that's not what people want to hear That's not what the gurus are selling you guys The gurus are telling you that they've got the answer and you should buy it from them And then you're gonna be successful. Nothing is a bigger lie than that Nothing is a bigger lie than I've got the answer and I'm gonna sell it to you now I've got courses. I've got training and it's good training and you can buy it and you can apply it But just buying my course without the application is not gonna do you any good. I promise you it will not guarantee success Paul says action speaks louder than words. You can't succeed on what you're going to do No, by the way started videos and we'll be posting this week Paul. We're gonna hold your feet to the fire I've been waiting for that buddy. I've been waiting.  Everything comes together when you work, doesn't it? That's when everything comes together, right? That's when your book comes out. That's when your web page is published That's when your course is sold is when you do something now I've given you the four things They they on surface are simple. They're simple things. There's nothing complicated There's nothing anybody with average intelligence couldn't do in those four steps however They're extremely difficult to continue doing. Okay, and if you force yourself when you don't feel like it If you really truly force yourself when you don't feel like it You have no idea how successful you can be You have no idea. I had a bad day yesterday at my food company I'm not talking about my courses or anything else, but not one of my companies $8,900. I had a bad day bad day. I would have never thought that was a bad day before Okay, I would have never thought that I would have thought that was an enormous day It's not because your benchmark will move your benchmark will move You know, my son recently graduated from school and he works at the Cleveland Clinic He's making good money and so is his wife and if he had made half that amount of money Prior to that he would have been totally happy with it. What happens is your benchmark moves So when I see $8,900, I'm like, oh, it's okay. I mean I can pay my bills, but it's not what I wanted Now it's not like that $8,900 is gonna make or break me. It's not Okay, because my bills are already paid. They're already paid But it's just in my head. I've got these goals these goals I want to do these things I want to do and when it's below what I want to do Then I kind of look at it, right? Let's see here the Queens within good to see you. Let's see here I've always wondered if I was looking Lacking because I didn't have a mentor though. I didn't see the necessity to look for one so true You don't need one. You don't need one I feel leery of following people because sometimes mentors get a power complex They do and what the heck is a mentor anything out because I mean I could tell you every single thing I do it It may not work for you. You may have a different way of doing it. You know Frank Kern is funny I love Frank Kern. I think he's a great guy And if you don't know him you can look him up, but he could give you every single thing He does and you could do the exact same things and you would not be successful Why is that? Well because you're not Frank Kern. You're not him You've got to do your thing. You've got to learn this stuff along the way Don't let not having a mentor prevent you from this number one, right? The number one thing I said don't let not having a plan prevent you from succeeding having a plan is just nothing It doesn't mean a thing, right? When I started out I started selling cell phone cases. I had no plan I had no plan on owning a survival food company or selling courses on email marketing I didn't you know what email marketing was how could I have a plan for that? Quit thinking that you need a plan. You don't need a plan. You don't need a mentor You need to do these four steps. You need to do these four steps Queens within says exactly you have to get a success plan that is tailor-made for your situation And yes, and you know who tailor makes that plan you do You do you figure out what you're interested in you figure out what path you want to go down You figure out the things that make you tick and guess what?  You don't need it It's okay to take advice and let me tell you who gets mentors You want you want me to tell you get mentors because I mentor some people for free when I see somebody and they've got unbelievable potential and They're putting in tons of work and they're just really working out it hard And I see something that they're doing that I know they can improve on I'm gonna approach them I'm gonna go to them and say hey, I see you working really hard I see you doing a lot of stuff This is the one thing that I would change if I were you and I think it's gonna work out better for you Just thought I'd put that bug in your ear.   They'll just say to you hey You know, that's the thing that I can help you out with that's what they're gonna do See, how is the dehydrated product developing going? I'm not doing that a whole lot right now Paul And the reason I'm not is because I can't get supplies Right now, you know, somebody said to me the other day Well, you want to sell your food to this country or that country this country I don't have enough supplies to supply the United States right now. If I got more sales I couldn't fill the sales. So right now that burner I mean, I can't I can't even get cans for some of my products right now So we're selling a whole lot more than we can supply so there's no point in adding more products at this point So that's why I'm not doing that. Just so you know And here's the other part. Here's the other part. Let me get my notes here Let me get my notes. Okay Here's the truth guys. This is the truth. I want you to listen to me. This is the big bonus the future Does not care about your past nor does the present Now, what do I mean by that? The future does not care about your past nor does the present Don't blame Everything that's going on now or everything that might go on in the future on what happened to you in the past It's irrelevant. The future doesn't care about that. There are way too many successful people that started off in bad situations I gave you an example of mine my friends You know when I was in the high school the neighborhood we grew up in the town we grew up in that has nothing to do with your future It has nothing to do with your present. It was just something in your past. That's it That's it. You can't let it affect you any longer.  I was in because of the things I had to go through. You know, you know, I couldn't do any better That's just not true. See what systems have you put in a place to help you daily and stay on track? Wow, I have so many of those fall that would be a whole another video I have so much automation to my business at this point. And by the way that that $8,900 we did yesterday I didn't do any work for it. I didn't do any work for none zero zero. This is money that came in now Truth being told I didn't do any work yesterday for it. But for years. I've done other work for it that built up to that Okay, that's what you didn't see what you didn't see was me staying home on the weekends working on stuff What you didn't see was me staying up to two or three o'clock in the morning working on stuff You didn't see any of that. You just saw the $8,900 yesterday from the survival food company. Okay, that's And it's and once again, I want to impress upon you. That was a bad day. That was very bad day But the point is you build on all these things as they go You do not you do not you do not dwell on the past now I look at the past as things. I was very lucky to have I had a tough childhood. I had abusive parents I had all that good stuff. I had you know bad neighborhood. I got in trouble. I got arrested as a kid I got a lot of trouble and I look at all of that as a benefit I look at all of it as a benefit because it makes me tougher now so that what now when I have to sit there and edit videos For two or three hours. I'm like, oh, I don't want to do this. Wow Remember when you were in the street and fighting.  I'm like that's beneficial as heck People who've never had any problems people who've never had any turmoil people who've never had anything go wrong Don't know how to handle it when it happens Okay, so all this negative stuff that you're hanging on to and that you're whining about and crying about They're extremely helpful if you allow it to be and they're extremely harmful If that's what you choose to do with it the future and the present Does not care about your past. It's you that does it's you that does you got to stop that? Okay? I love air here any of your comments about this training today This is something I've really been wanting to talk to you guys about because you know I it's so many technical things and you guys try technical things and you try sales pages and emails and all these things But there's more to it than that. It's your mindset. Okay. It's how you're looking at things It's what you're doing every single day. It's the effort you're putting forth Those are the things that are going to make the big changes. Okay all the other stuff is knowledge and you need it You need it, but you need this you need this training session today more than anything you can imagine More than anything you can imagine you need these four things. You need these so bad and you may not understand What breaks my heart is to see talents and people waste their talent and not do anything But they are first to complain that they aren't successful Yeah But anybody who is successful can see right through it You know, I can see right through it when people say they want to do stuff I have a person that is a warranty guy on a house here. He wants to do stuff online And he screws up the emails I send him, you know, and he says he's an entrepreneur and he has a job working for somebody else That's not the case. You don't okay. You're not an entrepreneur if you work for somebody else. I get news for you That's not the definition But entrepreneur an entrepreneur goes out on their own and makes the room money He doesn't do that and he screws up what he does do in his job And I'm not talking bad about this this company or anything. It's an individual. Okay. It's an individual didn't say his name But you know bottom line is I don't see it. He doesn't try hard enough. He doesn't put in the effort He doesn't put in the effort. God effort is it guys and once again Let me leave you with this the future and the present does not care about your past You've got a quick caring about it, too. I hope that works out for you. Hope this is helpful to you today Let's see here. What else I got in here epic instrumental music I completely respect what you say about not needing a mentor. However, oh, here's a however However, I have this idea that any way I can cut the learning curve With some help to shorten the time frame ideas. Yeah get training I mean, I've got courses if you like what I've got to say look at my courses other people have courses that are good out There and you're gonna run across some bad courses and some bad training Training you know, there's two ways to be successful online it's put in tons and tons of hours and learn everything or Spend some money and learn from somebody who's already done it find somebody you trust find somebody who's still doing it Don't buy a course from somebody who's not still selling stuff online if that's what you're doing The Queens within your health is important Also 100% that friend of mine that I talked about in the beginning of this training today is health is failing I can't walk very far. I Go to the gym as much as I can I work out. I'm not in great shape. I'm in pretty good shape I can do over 200 push-ups, you know, and that makes me feel good But yeah, if you don't have health you're screwed and besides that it's hard to have a good mental attitude when you don't feel well So yeah, we'll leave you with that guys. So I thank you so much for being here today Really, do you have great comments? You put a lot of stuff in here that is very helpful to the session Let me see what else we got here in the four-hour work week He talks about building systems and had to pay the price in the beginning. There it is Bottom line quit having a pity party and take action 100% fall. It's very true Feel free to put any comments in there if you're not watching this alive You can certainly put comments in the comment section below in the video a lot. Thank you so much I appreciate you being here, buddy. It's really nice to have you here. I love each and every one of you I really do I hope I'm not too harsh today, but I want to tell you the truth. Thank you for your time. Have a great week Thank you so much epic instrumental music and check out epic instrumental music I don't know if you have a channel on here or not guys subscribe to their channel Once again, don't forget to subscribe to my channel if I button now they're turning from red to gray Don't forget to ring the bell Turn all bell notifications so you're notified each and every single time I go live or I upload a video right now I'm uploading three videos a week and doing about three or four lives If you like this training share it with somebody else Please give me a thumbs up clicking that little thumbs up means a lot to me guys I and if you would just do that it takes you a second to click it And it really really helps a lot by the way, I love how Elon Musk provides internet satellite for you great That guy's amazing. He is amazing and we all love Elon release. I do okay And I think he's a really good example of hard work Guys, that's what it is take those four steps today applying and do some hard work Thank you so much for being here and I will see you next time which will probably be Wednesday


The superheating process is a unique grain refining technique which can be applied to aluminum-containing magnesium alloys. This process involves heating the material above its melting point without the addition of any external agents, resulting in the formation of small aluminium particles which act as nucleation sites for the growth of larger alpha-mg grains. These aluminium particles are believed to be responsible for the improved grain refinement observed in the superheated sample compared to the non-superheated one. This article was authored by Sun Si-yong, Yonggopark, and Yongchul Lee.


Hello, everyone. I hope you're enjoying the conference so far. Today, I'm going to talk about how we use Prometheus and its friends for an OpenShift offering in Red Hat. And I'm going to tell you about the system that we built for remote health monitoring for the OpenShift clusters. Also, how we get our telemetry data to make data-driven decisions in Red Hat. Without further ado, let's start. I work for OpenShift Observability and Monitoring team as a software engineer. Also, I am a Thanos maintainer and a Prometheus contributor. As a team, we are building a platform to collect and store observability signals. Also, we have SRE responsibilities, and we are on call for the internal platform that we are building. The system that we build is called telemetry. So what is telemetry? In simple terms, telemetry is an open source remote health monitoring system. Remote health monitoring works by sending a carefully chosen subset of data back to the telemetry service. The data is anonymized to maintain privacy. We do not collect any identifying information, such as user names and passport or resource names. And the full list of data collection is available publicly. So the primary information collected by telemetry includes the number of updates available per cluster, the number of errors that occurred during an update, the progress information of running updates, health condition, and status of OpenShift component that is installed on the clusters, the name of the platform that OpenShift deployed on, such as AWS. The data we collect enables us to provide a lot of benefits to the end user, otherwise it would be impossible. With the help of the telemetry data, we can observe events that may seem normal to a single end user, but with the perspective of seeing those events across the fleet of users, we can provide more insights. With the connected information, we can improve the quality of the releases and react more quickly to the issues found in the clusters. As a result, we provide better support. And the information allows OpenShift to more rapidly release new features and focus on engineering resources where they can be most impactful to the end users. So how we build telemetry. In Red Hat, we have an upstream-first mentality. So telemetry is based on the open-source tools on the Prometheus ecosystem. We deploy and maintain highly available Prometheus alert manager and several Thanos components in each and every cluster we run. How do we deploy? We use Prometheus operator to deploy Prometheus alert manager and several Thanos components into OpenShift clusters. So let me briefly explain what it is for the ones who aren't familiar with the Prometheus operator. The Prometheus operator provides Kubernetes native deployment and management of Prometheus and related monitoring components. The purpose of this project is to simplify and automate the configuration of a Prometheus-based monitoring stack for the Kubernetes clusters. By using Prometheus operator out of the box, we monitor critical cluster components and alert on the metrics we collect. Moreover, we also let the users define, configure, and deploy their own monitoring stack to monitor their own workloads. In this stack, we have Prometheus HA payer, alert manager, Thanos ruler, and Thanos square here to provide global overview for the Prometheus HA payer. So how does it look like? As I already told you, we deploy Prometheus using Prometheus operator and several other Thanos components. For each cluster, we collect and send critical metrics, critical alerts, and the information about upgrades to our telemeters service. In the first version of the system, the in-cluster Prometheus was collecting the data from the workloads in Prometheus format. And then a dedicated component was scraping the federated endpoint of Prometheus every four and a half minutes. Then the component was cleaning the metrics and anonymizing the data and then sending the metrics in Prometheus federate data format to the server side telemeters server. On the server side, we had the telemeters server component. It was receiving the data on the federated data format and stored the data into its hashing of numerous replicas. And then the telemeters servers had been scraped by the two replicas of Prometheus ingesting all of the data twice. The hashing was super primitive. All the data was in memory and nothing was persisted. And we used Prometheus to directly provide access to the data we collect. Ultimately, they were bottlenecks. And we failed due to the high volumes of data to ingest and query. Moreover, the queries of that would bring down one of the Prometheus's eventually and that would prevent us from ingesting more data. So when we hit these scalability issues, we decided to invest a redesign of the system. And we chose Thanos to build a more scalable system upon. Thanos helped us to compose a highly available metric system with unlimited storage capacity, which can be added seamlessly on top of the existing Prometheus deployments. Thanos helped us to build a cost efficient store for historical metric data while retaining fesquare relays. We introduced a new custom central metrics collection pipeline for telemeters using Thanos. To make it even more scalable, Red Hat invested in adding a new component to Thanos called Receipt. At that point, this was a novel idea for Thanos because it had changed Thanos model from a pool-based solution to a push-based one. This effort was started in June 2019. And we ended up building a SaaS offering for Red Hat's internal usage for internal customers. So how does the new stack look like? All the in-cluster bits remained the same. And we converted the telemeters server to a mere authentication proxy and data transformer. For the legacy endpoint, all the uploaded data now being converted to the Prometheus remote data format and send it over to the Thanos Receipt. We added an event point though. With the event point, we added the ability to support directly sending metrics from the Prometheus to a telemeters server using a remote write API. In the upcoming OpenShift versions, we are planning to move completely to writing directly from Prometheus to telemeters servers. Also, we created a new controller for Thanos Receipt to coordinate updates, adding and removing nodes from the Thanos Receipt hashrink. And we utilized Thanos Store and Queryr to provide access to the data to our internal customers. But how about the other signals? I mentioned that we collect them, right? So this diagram shows what we have now, metrics and logs. And what are we planning to build soon, tracing and profiling? To extend the functionality of telemetry, we decided to build yet another open source observability system. Our main goals were to provide multiple observability signal support, correlation between signals, seamless multi-tenancy, authentication, and authorization, and improved security. Thanks to Prometheus ecosystem and the other systems that were built similarly, this was a relatively easy task. So we based our design on two major points, schema-less labels and object storage support. We recently started to provide logging solution using Grafana to our internal customers at Red Hat. We called this system observatorium. You can think observatorium as a distribution. We have packed a reference architecture of Thanos, Loki, and soon others that allows easier installation, configuration, and operating of several observability systems that we found the most useful and practical. And of course, all of these efforts are open source. You can check out everything we have done so far by visiting our website. Please do so. So what we learned while building this platform. In such a big organization like ours, being able to offer an internal source for the less experienced teams, extremely useful. Moreover, the flexibility of deployment and design is must have. Requirements and priorities change overnight. And we had the support from one CPU cluster to many, many huge clusters. Using schema-less labels to correlate signals was paid off. API-derival model helped us to extend the system easily. And cost matters. And on an even medium scale, it takes millions of dollars to gather all the log lines, traces, or events. So focusing on actionable metrics helped us to scale seamless. And also relying on the object storages for long-term retention helped us to control all the costs. Last but not the least, we are currently hiring if you want to work with us and contribute to all these cool open source upstream projects, please let us know. You can apply by using the link in the slides. If you have any questions, please reach out to us. You can find us on the upstream projects, CNCF Slack channels. Thanks everyone for listening and have a nice day.


Thanks a lot, Pop. Anthony, you guys finished the homestand 6-0. How encouraged are you by the way this team is playing right now? It's nice. Oh, we had to walk off, sweep with the Indians, or I'm sorry, the Guardians, and the Orioles who played us well in Baltimore and just come back today. And it's just, it's nice. It's everyone is really settling into their routines and starting to come together a lot more. I think someone had a three-homer game in there too, as well, during this homestand. Do you remember that one? I do. I do. I just want to have good at bats, good team at bats, get guys up there, put us in a position to, you get a hit, you're driving in and runs. Speaking of good at bats, it's a tie game in the fifth inning and Zimmerman's pitching very well. 2-2 count, a guy who features a change-up. What's your approach in that spot? I just put the ball in play, not ever trying to do too much, and threw me a fast ball away. Just went with it, not trying to do too much. And that's when I choked up so much to try for the ball boy. Final question, David Cohn, who does the games for us in the booth. Former Cy Young winner said he watches you at the plate and you just look so relaxed. So how comfortable are you in those pinstripes? How comfortable are you in this stadium? It's just something where the little things, you know, before the game, behind the scenes, getting the routine done and staying with the routine and just come out and play baseball. It doesn't matter where it is, what field you're on, just want to play baseball and have good at bats. Thanks very much, Anthony. Thank you. I appreciate it.


Felly ydych chi'n gwybod yma ystyried yr aelodol am gyfer bywyd ein fwyaf ar ysgologiaeth o gyfnod am y welfa amsgologiaeth am y grifritol iawn. Fydd wedi amdano'r gyfer esgolwaldur Eamor wedi gwneud y dweud y pwysig recruiting. Ond ystod yn dechrau gyda'r perffordd yma. Ynno y pwysig, because rydych chi'n gwill yma ydych chi yn y ddechrau'n y pwysig. I'm just going to say a very brief few words and not delay getting over to the main event which is to hear from Artur Runga-Mesker from the European Commission. My name is Eamor Cotter. I'm a director in the Environmental Protection Agency and I'm really delighted to welcome Artur to Ireland on behalf of the EPA. He's been really willingly engaging with all of the events that we've set up for him today and this is the first event, this lecture here in the IAEA, which we're delighted to co-host with the IAEA and it's our first foray in terms of a partnership between the EPA and the IAEA and really hope that we'll have further collaborations into the future. Artur, another key part of Artur's visit is a public lecture that he'll give tonight in the mansion house and we have a big audience coming to that and we're doing that under the umbrella of the national dialogue on climate action. So, for anyone who's interested, we're streaming it live on the EPA website at 7, we're starting at 7pm, so tune in if you're not actually coming to the event. I'm really looking forward to hearing what Artur has to say today on the European Green Deal, on the plans for a climate neutral continent by 2050, the pathways, the building blocks to get there. Now is a really opportune time that we have this discussion, we're very fortunate to have him here so soon after all of these agreements have been put in place and so I'm really looking forward to hearing him speak and that's really all I'm going to say except to say thank you for being here Artur, thank you for all of your engagement and to say I'm pleased again we are in the EPA to be partnering with the IAEA so thank you very much. Dr Artur Rumkomatsger is a director at DG for Climate Action in the European Commission. His responsibilities include domestic and international climate strategies overseeing the governance of EU climate action regulation of greenhouse gas emissions from I think the non ETS sector and supporting innovation and modernisation in the energy sectors. So with that small mandate, could I invite Dr to address us please? The address will be on the record and we follow Europe House rules and the subsequent discussion. Thank you very much. Yes, thank you very much and thank you very much for inviting me to this, I'm told very historic venue and also to the Institute for European Affairs and tonight for an even bigger event and thank you all for coming. What I want to do is really to start off and looking at the vision for the year 2050 climate neutrality. I think this should work, yes. What we all notice and you will do as well in Ireland is that the European climate in Europe is changing. We see the effects of climate change everywhere in all parts of Europe and it's not only the agriculturalists or the farmers who see that because weather patterns are changing, the climate is getting drier in many parts of Europe and you really have effects on agricultural yields and also on forests dying in certain parts of Europe. No, it's also touching the industrial fabric in Europe because all power plants need water and if there's no water then it's hard for a power plant to run and for a lot of shipping in Europe, the inland shipping you also need water and we have had phases where the iron ore couldn't get to the steel mill and the chemicals couldn't get to the customers so we see real economic effects happening over the last years and that is only with an increase in global average temperature of one degree Celsius compared to pre-industrial levels. If you look at the report of the IPCC, the Intergovernmental Panel on Climate Change, they are saying the 1.5 if we continue as today we will pass between 2040 and 2050, two degrees will pass somewhere between 2060 maybe 2070 so that's a doubling even of that temperature increase. So we can imagine how this is going to change what is happening around us and that it will not only have economic effects but it might also unleash a lot of security issues because if the climate gets difficult people start moving and some of my colleagues working on development are saying this is what we are already seeing today that a lot of movement of people and migration is happening because of climate change so we need to do something against it and that is what the plan is in Europe to say we need to be climate neutral by the year 2050 and you see here the emissions how they would evolve from the year 1990 to the year 2050 and what you can see is first and foremost if you look at the end of the graph in the year 2050 you will still have emissions. You will have emissions from some areas where it's very hard to abate them or to get rid of them and one of those is agriculture because cows will continue to do what they also do today but there might be other areas like for instance aviation where we will not be able to fully decarbonize that sector by the year 2050. So in order to get to net zero we need to have removals on the other side and that's also where the agriculture, the forestry sector can be quite instrumental because plants and soils they can suck up carbon dioxide from the atmosphere and can make sure that the balance gets to net zero. On top of that we will also have industrial ways of capturing CO2 from the atmosphere which we can put into geological strata, you see it here in red and what you also see is in terms of the different colours which show the different sectors of the economy all sectors will have to contribute and just look at the yellow one which is the energy sector already by the year 2040 will be able to fully decarbonize it if we want to be climate neutral by the year 2050 and I think that's one of the sectors where it's easy to imagine what types of technologies we are thinking of because those renewable energy technologies are already around and other carbon free forms of energy production are also there. What is also important to note is that this is not a straight jacket because we have many technological options that you can deploy in the different member states and you can adapt to your local, your national circumstances how much you are going to use any of those seven building blocks and it starts with energy efficiency. If we were to move forward in an optimal manner we could reduce the energy consumption in Europe by half by the year 2050 without reducing the level of comfort for the citizens we can even use more energy in a more productive manner. One of the big issues is buildings in 2050 the houses you will see probably 70 to 75% of those houses are already there today. So a deep renovation of the current building stock is something that is absolutely necessary. Again that's not rocket science because most of the technologies we know already today it just needs to happen and that will require efforts on the policy side. We know the renewable energy I've been talking about but then there's also a few member states who will embark on nuclear energy in order to have energy that is going to reduce the carbon footprint. We will use much more electricity because that is the most efficient way of using energy in the whole energy system. And you can turn this electricity also into other forms of energy for instance into hydrogen into synthetic fuels for processes where you need for instance high heat and other things. What I want to say here is that in terms of the technological solutions I think most of them are known many of them will still need to be tested at large scale. Reducing the use of fossil fuels also comes with a big benefit in terms of our trade balance because most of the fossil fuels we have to import in Europe so we will have a positive impact on the fuel bill. The same I think is true when we look at the sector of transport mobility. Today you see here on the left hand side in red 95% is dependent on oil. But in future in the year 2050 on the right hand side you see a much bigger variety of fuels that you can use and that get the whole sector almost completely decarbonised except for that little red bit on the bottom there. Which is kerosene for aviation which we might still need in 2050 but even there technological progress might be faster than what we expect here. When it comes to cars I think that is the next biggest revolution that we are going to see in the transport sector and that is the electrification of cars that is going to happen in the next 10 years. But that also will require a lot of infrastructure that needs to be built again government help working with private sector handing glove in order to move there. The next building block is industry and here you come sometimes to emission if you don't look at the energy emissions from industry that are very hard to abide. The prime example is always steel. You need a lot of coal and coke in order to reduce iron ore to produce steel. But we also know that we can use instead of that hydrogen and you can produce hydrogen in a clean manner. So one of the task is to look at these processes and test these technologies at a large scale in the next 10 to 15 years in order to roll them out in the years thereafter. And if you look at European steel industry it needs a complete refurbishment anyway in the next 30 years so from that point of view you will also modernise the European economy. A second thing is the circular economy which means at the present point in time a lot of the material comes into Europe and then goes straight to the waste bin after the productive life of the products. And that also will have to change. We have to make our economy in Europe much more efficient and much more productive in that sense. So the circular economy action plan and so on is something that is going to get us forward on that route. Another aspect of this is the regional aspect because industry in Europe you will find in big clusters. And the good thing there is that if you have a lot of and very different industry in a big cluster you have huge possibilities and linking these industries up with each other. So what is called sector coupling is something that is key for the circular economy and in some of these big clusters what we need to see in the coming 5 to 10 years is some of these big projects where you show how you can bring these different sectors together. The energy sector with the different industrial sectors in order to come to circularity. That will require a completely different type of infrastructure and that is not only the electricity grid but you also talk about the gas grid which is a huge asset in many parts of Europe which you can use for decarbonised gas in 30 years time in order to get to net zero and climate neutral 2050 in Europe. It also means the whole transport infrastructure and on top of that in order to manage it efficiently it means digitalisation. So this integration of the different networks in Europe is something that has big chances in moving us forward to decarbonising the economy and to get to climate neutrality. Another key block is going to be agriculture and I know that is something that is always very important in Ireland. And what I tell farmers always is that farmers are not going to go out of business if we want to fight climate change. On the contrary it will mean more business for farmers because it is not only that we will have to feed many more people by the year 2050 but we will also have to produce many of the feedstocks from agriculture products or forestry products. So material substitution whether it is in the building sector whether it is in the chemical industry in terms of feedstock where today about 10% of the imported oil is being used as feedstock that can be replaced by agriculture products. And it means in the end that also agriculture will become more productive than it is today. So there is also a task here to make sure that we become more efficient in agriculture across Europe. Finally I mentioned the removals because also their agriculture and forestry can play an important role. So we need to value these things that we have whether it is in the soils or in the forests and we need to reward farmers if they do more than what they did in the past. And I think that's a big policy task in the coming years. Then the final the residual I spoke about that already is carbon capture and storage. So apart from the biological way of doing it also look at industrial ways of capturing carbon in Europe in order to reduce the amount of CO2 in the atmosphere. The extent to which we will need that will depend very much on how far you will get the emissions down by the year 2050. So that is what you can also play with in the whole policy. In order to get there we need to invest massive amounts of money into the European economy. We reckon that it is somewhere between 150 to 170 billion euros every year from this year up till the year 2050. That's about two percent one and a half to two percent of our GDP. If you compare that what we invest in any case every year into our economy which is about 20 percent of the GDP. You will see it is a significant amount in terms of the increase of investment. And we also need to make sure that the 20 percent you spend in a way it's compatible with the climate neutral world. And that I think is a big task for policy in Europe together with the private sector. If you invest into your economy you usually create growth. So what we want to do and that is the vision behind it that this is going to be the new growth model for Europe. Here you see in the graph and it starts in the year 1990 how economy has been developing. We have been increasing our GDP by around 60 percent since 1990 at the same time emissions went down somewhere around 23 24 percent until today. And we want to continue along these paths into the future into the year 2050. Every transition comes with challenges for different sectors. And I think the one everybody knows immediately is the one of fossil fuels. Whether it is coal whether it is peat whether it is shale gas or shale oil in Europe those sectors the pressure will be very high. Because as I said in the beginning decarbonising the energy sector means that we have to change electricity production fundamentally in that sector. What is important is that because many of these activities related to fossil fuels are very focused on particular parts in Europe. You have it in the midlands here in Ireland you have it in Poland in the southern parts where you have coal different parts in Germany northern Spain. And we need to make sure that we help these regions through the transition and we have to be very proactive. But that is kind of the task in the energy sector for the coming 10 years but it goes much further because it also means all energy intensive industry. Whether it is the production of automobiles or trucks all of that will have to be changed will have to be refurbished over the coming three decades. And we need to make sure that we do not leave anybody behind and that's also equally valid of course for the farming community and for the foresters where you will see a lot of changes. I should not leave finish this presentation without not saying that of course this has a global dimension. Europe's emissions are eight nine maybe 10% internationally so even if we were to close down our economy tomorrow we wouldn't do any change. We wouldn't see any change in terms of the speed and the pace of climate change happening. So we need to work with other countries around the world. We need to encourage them to follow our way. And I think we see this happening. Look at renewable energy which has not only become cheap in Europe. It has become also the most cost efficient technology in many other parts of the world and you see a huge roll out of renewable technologies around the world. You also see it in terms of regulatory action. Emissions trading is not something that's confined to Europe. China is building its own system at the present point in time. So also on the regulatory side we see things happening. South Africa is introducing a carbon tax and it's going gradually from one part of the world to the other. Still in Europe we think that in the coming years we'll have some particular changes, challenges and that could lead to what is called carbon leakage. So this is something we will have to address and see is it necessary for certain products or sectors to look at carbon border adjustments yes or no. So that's one of the questions we are posing ourselves. And this vision is the starting point for the Green Deal that was just announced by the new commission. Because that is what the commission wants to do and the new commission in particular to say we need to start with the first chapter in this transformation process. So the Green Deal as it was announced just before Christmas is trying to do this to set an agenda for the next 15, 18, 24 months to say what action do we need to do and all the sectors I have been showing in order to move forward towards a climate neutral Europe in the year 2050. And that will have to include also the issue of finance in terms of how are you going to finance this change. And I'm not going to go through each and every. If you come tonight you will get a much more detailed view of this particular graph. But what I want to say is we will be looking at action in all the different sectors. So it is the climate legislation, the emissions trading where we want to move the targets up from the current minus 40% to minus 50% or minus 55%. You will see it in the energy sector where we will look at new initiatives on offshore wind, on new initiatives in terms of the networks that we are investing in in Europe. The same is true for industry where a circular economy action plan is going to be developed and will come out in the coming weeks that we start grabbing these lead markets for these new products in the industrial sectors. The same will do in terms of mobility, the roll out of alternative fuels infrastructure which is so eminently important in the coming five years is one of the big things. We will look at the farming sector in order to make farming more productive, less harmful to the environment. We will also look at preserving and restoring ecosystems because it's not only a climate crisis we are in. We also have a crisis with respect to biodiversity that is going to be addressed. There will be a strategy coming out in about a month's time and we will look at other areas of pollution, whether it's the chemical sector, whether it's the traffic. So the pollution standards we have for cars, they will be ramped up again going to Euro 7 standards. So it's a big work program that is coming forward in the future and all of that will have to be done looking at also the social issues that come with it. Don't leave anybody behind and that's not only in terms of giving people money, it's also bringing people together. What has been announced is a climate pact where we want to work with civil society, with citizens, with business corporations, with farming associations to see if you have good ideas and you can inspire others and you want to work on those ideas, let's come together and do it together and create little hubs of progression to move forward. So that could also be cities. At the moment we already have a network of mayors, 6,000 in Europe and we want to expand that work in the coming years to take all the citizens with us. That's the green deal, so it's a big task not only for us in Brussels because it will only happen if also member states and as I said regions, municipalities, the business, the farming community, schools, kids all move together into one direction. It's a big project in terms of transformation. We should not be mistaken. This is not going to happen like manna is falling from heaven. It requires roll up your sleeves now and then continue for the next 30 years working on it. Thank you.


Greetings, everyone. Welcome. My name is Nanakshi Kaushik and I'd like to introduce my friend and co-presenter, Nilema Mukheri. We both work at Cisco and we are responsible for strategy and execution of two container platforms. We see increasing adoption by our customers for machine learning, and that is a reflection of the current world where increasingly machine learning models are used for making critical decisions. For example, machine learning models are used first for hiring and then for filing. Similarly, machine learning models are used to grant FDA approval of a COVID-19 vaccine and also to decide who gets an ICU bed. All these have real-world implications, and obviously we want our models to be as perfect as possible. However, we increasingly see headlines like these. Since these decisions are made by a machine learning model, it is easy to measure them and measurement leads to awareness and awareness leads to change. How do we define fairness and bias? As you can see from the popular definitions I took from the web, it is very subjective and it depends on the stakeholder. In this presentation, we will assume that the stakeholder has decided what their definition of fairness is and we are going to look at sources of unfair algorithm bias and how to make the outcome fair and provide conclusion. How does a bias creep in? This is a popular way to build a machine learning model and deploy it in production. Now let's take an example of a stakeholder.  So they hire a data scientist and give him a census data to build a machine learning model. The data scientist builds a model and finds that his model can provide 80% accuracy. They come back to the bank and the bank accepts it and deploys the model in production. User starts interacting with the model and it does exactly what the data scientist said. It is accurate 80% of the time. However, within a subgroup, female are getting disproportionately rejected for loan even though their salary is greater than 50K. And on the other hand, the males are getting accepted for loan even though their salary is less than 50K. So even though it is accurate in the overall population, it is not accurate within the subgroup. So how does that happen? If the data was perfect and the machine learning model was perfect, it would have been equally accurate within the subgroup as well. However, data is never perfect. So like any other algorithm, we go for a little bit of exploration and debugging. Fairness is an upcoming and a very popular topic and there are many fairness toolkits available. We have looked at these four toolkits and the libraries which they provide are easily integrable into your machine learning pipeline running on tube. For data exploration, I picked Whatif tool because it provides an interactive analysis and it is easy to look at what if conditions. But the other libraries are pretty great as well which we will be looking as we look through different other examples. So with that, let me go into my on-prem Kubernetes cluster where I have deployed Kubeflow. Let me start by giving a quick introduction of Kubeflow. Kubeflow is a popular machine learning lifecycle manager and it's also used by many of our customers. It pretty much contains all the utilities you need for your machine learning task. For example, it consists of a notebook server for notebook exploration, a hyperparameter tuning library, ability to run your experiments and finally build pipelines. So for running the widget library from Whatif tool, I'm going to use my notebook server. One thing which I wanted to point out is that to run the widget library, I ran into dependency issues using the stock Jupyter lab 3.1 images provided by Kubeflow. So to get around that issue, I just built my own notebook server and then I had no issues running the widget. So with that, let me just go to my notebook server and look at how a machine learning data scientist would use this. So the data scientist initially would look at the data which is a UCI census data from the 1990s and it has all the columns which you would expect from age to education to occupation and the number of hours per week and whether their salary is greater than or less than 50k. And then they build a quick model, they deploy it in production as I mentioned and now we are seeing that there are gender differences, even though the overall accuracy is the same. So then the first thing we do is start exploring the features. And since there are gender differences, we look at the gender and see if there is a discrepancy in the number of data points for that gender. So we can see that the male data points are way higher than the female data points and that would mean that the model generalizes for the male population. And that could be one of the reasons where the bias creeps in. The second thing is that other aspect which we normally do with data is we do feature engineering and really more for the data which has non-uniformity or non-uniformity. And so we can see that for example, the capital gains is mostly zeros and then they are from one spectrum to the other. So we might have to do some kind of feature engineering perhaps to change that. And that is another place where the bias may creep in. And obviously there are many ways in which we can pick the data which we want to feature engineer to clean the data set. One way to look at that is counterfactual. What a counterfactual is is a data point which is closest to the data point except in the one dimension. So for example if I pick a blue data point you can see that there is a counterfactual here. And for example in this example which I pick you can see that most of the fields are almost the same except that the field which has some differences is this field. So then that gives a little bit of a helpful indicator as to how we can fix how we can feature engineer that data. Now let's look at the models. As we saw that as I mentioned that the model has an accuracy of over 80 percent and which is pretty good. But then based on the for genders it gives us slightly different results. So we can then now go deep into what is the difference between the genders. And as we are digging deeper we can see that the female accuracy is higher than the male accuracy. However the number of false positives for the male are higher than the female. And that is what we see in the real world for example that the even though the male population has income less than or even though the real income is less than or equal to 50k the model is predicting that income is greater than 50. And so this is this is this is an easy way to look at where the bias is in your data set. Then to just give a give an idea about how we can change that we are going to look into rest of our presentation. But one way to look at it is the classification. If we change the classification threshold depending on where how the stakeholder in this case the bank decides the fairness is we can we can achieve that. But there is a trade off. So when we change the threshold the accuracy gets changed as well. So for example if you wanted to do demographic parity as you can see that changing the threshold achieves that but then the there is a slight trade off in accuracy and similarly with other other option. So with that let me go back to my slides to recap there can be many sources of bias. So how can we mitigate bias in our existing framework. Let's take a quick look at our existing framework. Now the stakeholder will add an additional fairness criteria for example bank deciding demographic parity. We would measure for bias in our in our pipelines and then also make our pipelines fair and finally provide the user with a specification under which our model would work. So for example COVID-19 vaccine would provide 95% efficacy if the two dosages are taken 30 days apart. One thing which I want to point out in this framework is that when we are mitigating bias it likes security you want to shift left. So you want to do it at the earliest stage. So for example if we fix our world then we do not need any fairness criteria. If you are not able to fix the world then if we can make our data perfect then you don't have to go later on in the pipeline to make your model. So with that let's take a quick look at how we do the define the equity and do measure and bias detection. We took a quick look during exploration phase where instead of looking at the entire population we look at the subsection of the population. Even for the subsection of population we can go as much deeper as we want. So for example it could be parity between blue and orange population or it could be a subsection within the population. So for example the ones who were denied loan but would default or were granted loan but would default and that would help us pick a appropriate matrix for fairness. With that I'm going to hand it over to my friend Nelima so that she can walk you through the rest of the presentation. Thank you Meenakshi. Hello everyone. Let's now take a look at how we can improve fairness in your machine learning pipeline. Let's say you're trying to build a machine learning model to identify dogs and you've trained it with lots of cute pictures of golden retrievers. It's not going to do very well when you give it a different type of a dog. So to be able to identify any kind of dog you want to feed your machine learning pipeline a diverse data set. So the first step in your machine learning is data collection and you start to look for an fairness right from there. Once you have data that's that you've collected you can explore the data to see if there's any bias in it. This is an example where we have two groups of people the yellow group and the blue group who are applying for a job. Your job selection process seems to have a bias here towards the yellow group and we have selected more yellow people than blue people. Maybe our fairness metric as a society for this specific job is to have equal representation among all data sets. So we pick a fairness metric called demographic parity and we try to optimize for that. One mechanism to do that is using relaveling where we rank all the people that we have available within each subgroup and then we take the lowest ranked person selected from the majority group and set them to unselected and you take the highest ranked persons from the minority group and mark them as selected. By doing that we see that we have balanced the selection amongst the blue group and the yellow group here. So we have equalized the outcomes irrespective of the demographic that the person belongs to. There are many other preprocessing techniques and depending on the situation and the data you may want to select one of these. So for example if you don't have a lot of data in one category but you do have data in another category you may want to do resampling to balance the data across the different demographics. Once you have preprocessed your data we get to the model training phase and we can also try to improve fairness as we build our model. There are multiple mechanisms available here like regularization, constraint optimization and adversarial debiasing. When we try to stop a model from overfitting that's in itself is a form of debiasing. This is a step in your machine learning pipeline which can be parallelized very well. The current open source models that we have open source packages that we have available to do debiasing are not really optimized for distributed training on Kubeflow and this is a place where we feel like we can have more work done. So for example if you take constraint optimization that's a technique that's very similar to hyperparameter tuning. In hyperparameter tuning you take your learning process and you change the parameters to your learning process like the step size for a gradient descent algorithm. In constraint optimization however we are not changing the inputs to your learning algorithm but you're adding additional constraints in addition to your optimization problem where you're trying to optimize for accuracy. So the methods used here are very similar to hyperparameter tuning and we feel like this is a place where Kubeflow can be easily extended to support distributed fairness bias reduction and like to see work happening there. Once you've trained a fair model we get to the serving process. Before we serve however we always want to analyze to see if there's still any form of bias that is leftover and we can do debiasing even at the point of before even before we start serving which is called post-processing. You can do this by changing thresholds. You can also have a fairness in mind while you're serving your model. You can do this by building interpretable models and publishing model explanations using things like model cards which describe what your model does, what are its limitations and what are its biases today and how you want to see it improved. Now let's get to an example. So here we are back to the Jupyter notebook which is running in Kubeflow on an on-prem Kubernetes cluster. We are going to look at the UCI dataset again. So we have the income across different categories plotted. So we have here income across genders and you can clearly see that you have a higher number of higher proportion of higher income people amongst men versus women. You also have a difference in the number of data points that are available for female versus male. With this kind of imbalanced data you would expect your model to have a bias. Let's check that out and see by building a model and evaluating the fairness metrics. So we use a decision tree classifier and we train the classifier on the data that we have. We then use it to predict the income level on a test dataset and we plot the dashboard, the fairness dashboard, using Fairline. You can see that there is a pretty big disparity in recall across the two genders. So you have a higher proportion of men getting over prediction or prediction of higher income when they did not have as compared to women. You also have a disparity in the absolute prediction as well. So you have a much larger percentage of men getting higher income predictions as compared to women, which is very intuitive given that our data was skewed towards a larger proportion of men having higher income as compared to women. Now let's try and apply different debiasing steps here. So we start with preprocessing and in preprocessing we take a correlation remover which takes a sensitive property and tries to remove the correlation to that sensitive property across your dataset. Here we have specific gender as the sensitive property and once the data is transformed to remove any correlation with the sensitive property, we go ahead and fit the model again and we try to predict based on this new model that we've built. Looking at the Fairline dashboard, we see a definite improvement in the recall. So you have a similar proportion of women being given over prediction as men. However, the disparity in predictions hasn't really improved much. You still see a lot more men being given a higher income classification as compared to women. So preprocessing has helped but not very much. Let's see if we can do something while we are building the model. So here's something we can do during in processing. We take a constraint optimizer here. This is we've selected exponentiated gradient from Fairline and we specify the constraint to it which is trying to achieve demographic parity and we pick the same model as before and run it through this constraint optimizer and try to reduce the bias. Again, as mentioned before, this is where Kubeflow would be super helpful if we had the ability to distribute different runs of the constraint optimizer across different nodes in your Kubernetes cluster. But let's see how the performance is. We really don't see an improvement in recall. You still see a high disparity between women and men in terms of the over prediction versus under prediction. However, what is interesting to note is now we're seeing a higher percentage of women getting higher income prediction as compared to men. This does not really intuitively make sense given the data we saw skewed in the other direction. But then this is what the model had to do to reduce disparity between the two genders and which is very clearly reflected in the selection rate disparity, which is pretty close to zero. So yes, in processing helped a little bit, but it's also introduced new disparity. So what about post processing? Can we do better? We picked a very simple post processing method called threshold optimization. Again, Fairland makes it very simple with a threshold optimizer, which takes as a constraint the type of fairness metrics that you're evaluating for. And here, again, we've selected demographic parity. And we build the model and see how it performs. You can see that it's selected different thresholds for zero, which is female and male categories. And the performance seems to be much better. The disparity in recall is under 2%. You have a similar proportion of over prediction and under prediction for male and female. The disparity in predictions is very close to 0%. So you're actually having equal proportion of positive predictions for both. So instead of trying to fit a model that is fair, given the data that is not fair, it looks like setting different thresholds did the job better. The post processing seems to be doing a better job in cases where your data has imbalance or bias that is built in. Now, are we done? Not really. Once we've built a model, you want to understand how it works and why it is doing what it is doing. The simplest way to look at it is building interpretable models and looking at their interpretations. In this case, we have a decision tree classifier and we're trying to see the importance it gives to each of the features. Surprisingly, it selected marital status and not the sex as the higher importance feature. So it's very much possible that we looked at the bias between single people and couples. You might have seen the data skewed much more than we see across the genders. So it is very important that at every step of your pipeline, you look not only at the data, but you try to identify the different biases that are there. Instead of coming with a preconception of, hey, there's a gender bias and we're trying to remove it or, hey, there's an age bias and we're trying to remove it, explore the data, explore your model and find out what kind of bias really exists and understand and explain so that even when the biases are not easy to remove, you still are making it very transparent why the machine learning model is making a better prediction. This is where machine learning can perform better than humans because it's not always easy for us to explain why we are taking a specific decision as compared to a machine which can be very explicit about, hey, these are the parameters I've used to make a prediction of positive or negative. With that, let's go back to the slides and summarize. To summarize, today we looked at different ways to improve the, improve fairness in your machine learning pipeline. Starting with defining the problem, collecting the data, measuring bias at each step of your pipeline, improving and building a fair machine learning pipeline and building interpretable models that users can understand. There are tools available today that make it easy to improve fairness at every stage in your machine learning pipeline. We think that there can be work done to improve integration into Kubeflow as well as to make things more scalable within Kubeflow. With that, thank you.