I almost bit it there.  We talked about super users, and super users have tools. Super users on systems, they have utilities that allow them to do things and perform actions and make changes in those systems. And I think that from our perspective, we feel like we've had a lot of progress in the development community, and the developers have infrastructure and tooling and processes, and we want to start to introduce some of those on the user side as well. And some of these things have kind of been in progress for a little while. You may remember that we actually printed a book starting last year that was written by a number of operators of OpenStack, the OpenStack Operations Guide. We have some other books that are coming out from O'Reilly, and we started some other things. Troy mentioned that we did an operators meetup in March in California. We have the ops meetups happening here this week. There's also another session on Wednesday. We've talked a lot about software and development and kind of that forward thinking, the future of the industry. But at the same time, there are a lot of enterprises who are making use of OpenStack. And there's a session on Wednesday. It's at 2 PM. It's in B407, up a couple escalators here, that is going to be focused specifically on things that we can do inside of OpenStack to make it more enterprise ready. So if that's something that interests you, please go to that as well. And then through this week, we have a number of different ways that you can start your journey to becoming a super user if you aren't already. There's a booth up the escalators just right out here that has a number of stations where you can start to sign up for some of the mailing list, join the community, provide survey feedback on OpenStack. And we actually have some of these books there that we're giving away if you'd have a copy of the OpenStack Operations Guide. And so we're trying to give you opportunities to start your journey as a super user this week. In addition to that, though, we want this to be something that goes on well past the summit. And one of the other things that we are launching today is a new online publication, OpenStack Super User, that is built for operators. It's by operators. Most of the contributors are users and operators of OpenStack Clouds. The editors are. And it's really going to dive into issues around running OpenStack, but also, I think, just as importantly, the kinds of organizational issues that people run into as they try to bring this change into their companies. And so this is, I think, something that's going to be exciting and valuable. And this is available at superuser.openstack.org with quite a bit of content already up there. Now, this is something that we're just getting started. And we want your help. We want your involvement. We want your feedback on what we can do to make this very valuable for you. So please check it out and let us know how you want to be involved and what you want to see there. I showed this slide earlier. These are the sponsors from the summit. And that's a lot of companies. One of the great benefits of OpenStack is this ecosystem. It gives you many on-ramps to taking advantage of OpenStack, but at the same time, that can be overwhelming. And one of the number one questions that we hear at the foundation is, how do I get started? Where do I go to get OpenStack in my environment? And so one of the other tools that we are introducing today, it's a new tool to help navigate that ecosystem. And so we are launching OpenStack.org slash Marketplace today. And this is a section of our website that's really oriented around the paths to adoption. So if you would like to consume OpenStack on an hourly basis as a service, it lists out public clouds. If you would like to have someone help you build a private cloud implementation, there are services and consulting companies. If you're looking for a distribution, there are lists of the distributions that are available, appliances that are available. And this is really meant to be a resource to help people find their way into OpenStack and be able to get the most use out of it. We launched a smaller version of this about seven or eight months ago that was focused on training. Because one of the number one pieces of feedback that we heard is that people needed a way to educate themselves or educate their teams on OpenStack. And since we launched that, it was again, it was a fairly small start, but it has ramped up. And the organizations that have offered training through the marketplace have delivered over 250 training sessions in 30 countries in six months. And so that's a lot of OpenStack education happening all around the world. And that's the power of this ecosystem. So we are expanding the marketplace today. And it's going to continue to grow. This is, again, just the start. And I'm really excited to see how this plays out and grows over time. In addition to the online marketplace, we wanted to bring that here in person. And one of the things I think is interesting, you heard the keynotes from Rackspace and Dell, and they talked a lot about working in this ecosystem. And the OpenStack ecosystem is, I think, an interesting environment. It's kind of like a farmer's market in a way. You go to a farmer's market, and you can buy things in different formats. You can get ingredients to go make your meal. You can buy pre-made meals. You're going to make a choice between those options there. But at some level, the vendors who participate in that farmer's market, they have a common goal. It may be locally sourced, maybe organic. They have something that is binding them together. And that's really, I think, what we see in OpenStack. We have companies who are out there competing in the marketplace and who are delivering services. But at the same time, they come here to this summit. Their developers go to the design summit. They collaborate. They work together. And so our companies compete, but they also collaborate to build the best technologies for the benefit of the whole industry. And so this is really the theme behind our expo hall this time around. We have the online marketplace, and we have the live and in-person OpenStack marketplace, which as you walk out of here, it's just next door over this way. And it was a pretty cool build out. So we actually took some time lapse video because time lapse video is a lot of fun to watch. So let's just roll that and see us putting that together over there. So our sponsors are over there. It's really exciting. I love that flyover footage at the end. Our crew actually has a drone with a camera on it. I asked them if we could bring that in here and fly it over the audience and get some overhead shots and they said our insurance would not like that. However, we also got the physical marketplace. We have the online marketplace. They did put together a quick time lapse video of building the online marketplace as well. Let's look at that. OK, not quite as exciting. So the marketplace is over there. And there's going to be a booth crawl and happy hour this evening. There's a game. If you get the guidebook app on your phone, you take some snapshots of QR codes to try to figure out a phrase. If you figure it out and you tweet it, you have an opportunity to win a trip to our Paris summit. So you get access to the summit. You get your hotel room there. And I think that's pretty exciting to have. So the marketplace is, I think, going to be a great thing for people who are getting started with OpenStack. That wraps it up for today. I'm really excited to have everyone here. Go explore the show, attend the sessions, get educated, get connected, meet people. It's going to be a great week. And thank you so much for being here and joining our community.


From Hollywood it's time now for Money dollar Billy Underwood Johnny they're trying to get you all over town Hotel said to call you at this skyline number. I'm out at Arnold Bennett's house. He's been shot. What right? Well, who shot him don't know yet. I'm a transfusion here before they take him to the hospital Look Johnny, I got something to show you know what some ashes. We just analyzed The Bennett building was fired by a pro. He used celluloid and a wick made out of paraffin. I can prove it I hope Bennett lives to hear that Tonight and every week day night Bob Bailey in the transcribed adventures of the man with the action-packed expense account America's fabulous freelance insurance Investigator us truly Johnny dollar Expense accounts submitted by special investigator Johnny dollar to the fourth state fire insurance corporation 4065 spear Boulevard Hartford, Connecticut The following is an accounting of expenditure is during my investigation of the Bennett arson fraud Arnold Bennett was removed to the hospital where he was given a 50-50 chance of recovering from a 38 slug that had entered his chest There was no weapon lying about and no witnesses in the remote hilly section of San Francisco where he lived to give any information concerning the attempted murder The police were more anxious than ever to find Tony Midas the man Bennett had put the finger on earlier The reasoning was that if he could burn down a building worth half a million to get back at Arnold Bennett He also might shoot him. I Told Andy court about underwood's findings when the police car got us to the scene of the shooting So you Lloyd and paraffin Wick. Did you say yeah, that's what bill underwood said on the phone Well, then it would point away from Tony Midas. He was an embezzler not an arsonist. Maybe Who's that policeman over there? Oh, that's an inspector thinking why don't I talk to him about it for a while He said Midas lived in San Quentin with a man named Hanley a professional burner Well, Hanley could have taught Midas a few tricks of the trade. Yeah, it's possible to him I don't know at least underwood is sure that he can prove it had an incendiary origin Well, that's the first hurdle Maybe we can't tie it to Bennett at that Johnny if Midas did it now Let's wait and see what Bennett has to say when he can talk. I think I'll get on over to the hospital Okay, oh, uh here's something that came up. Yeah, you said Bennett attributed everything to this Tony Midas Well, there might have been something personal in it too Midas is married to Bennett's niece Elizabeth What yeah, she married him a month before he was convicted Well, that might explain some things Yeah, she called me tonight and said she had some information for me about Midas I was on my way to see her when this happened. Oh, you haven't talked to her yet. Oh, no, let's see It is 1038 Murata Drive. What if that's far from here? No Johnny not far at all This happens to be 1038 right here. Oh, we better tell the police about her Andy Andy cord went on over to the hospital to await results on Arnold Bennett. I Spoke to the inspector in charge and told him the information about Elizabeth Bennett The police added the name to the APB already out for Tony Midas, and that's the way the case stood at midnight By morning the hospital reported that Arnold Bennett would recover from the gunshot wound Elizabeth Bennett had not been located nor had her husband Tony White always fix my own dinner poached egg and half and half ulcers Names dollar. Yeah, and that's right Insurance investigator and want something do you Coffee maybe no, thanks, mr. Angle mind if I finish go right ahead What led you to me? The notations about the trial mr. Angle you were the defense attorney for Tony Midas Or anxious to talk to him I defended him yes, I Don't think I'm going to be much help dollar. I haven't seen him since he got out of no idea what he is We'll find him mr. Angle and what's it all about? Well, Tony Midas has been identified as the man who started a fire in the Bennett building or At least who was seen in the vicinity of the building when I went up. I'm sorry to hear that Are you mr. Angle? Tony Midas was a nice kid who got in a little trouble everything was against him at the trial Bennett poured it on He didn't have to but he did he could have let him off. You were Midas's lawyer Did you try to talk Bennett into letting Midas off? No, I didn't nobody talks Arnold Bennett into anything. Oh Tony never would admit taking the funds. He said he was framed But he didn't have a prayer with all the evidence against him. Yeah, I read a transcript of the charm Then you know Tony Midas pleaded not guilty in the face of everything and he went up I wanted him to make a guilty plea and rest on the mercy of the court. It was his first offense Well, he's out now and it looks like he's trying to get even with Bennett for prosecuting all for a lousy ten grand Did he ever get in touch with you? I told you no no phone call. No Do you have any idea where he'd be in town mr. Angle? No, I don't Okay, then I guess I leave you to your eggs dollar yeah, if If you find Tony Midas, I'd like to know about it Why mr. Angle just curious. I'd like to see him. I'd like to see what five years in prison does to a kid like that mr. Angle Arnold Bennett was shot in his home last night. No That's all you have to say. What else is there to say? Well, you could have asked is he alive or is he dead for one thing? Suppose I don't care Okay, he's still alive. They think you're pulled through What do you think shot him mr. Angle? I don't know they're looking for Tony Midas for that too. Oh Did you know Bennett's niece Elizabeth? Yes, I met her looking for her too. She's married to Tony Midas. Yes Yes, I knew I knew about that. So now mr. Angle Oh, what is this going to be an inquisition that egg and that half-and-half doesn't interest you no matter How much you look at it? You ought to leave me alone and go find your firebug. Come on Let's have the story. I don't know any story to tell you was it spite that sent Tony Midas to prison because of him and Elizabeth Bennett. No, no they proved him a thief I'll throw one more thing at you angle Bennett wasn't always too good about paying his taxes our accounting man hasn't pegged pegged him for exactly what he is an Opportunist a Dodger a man out to get what he can for as little as he can a matter what Yeah, we cover everything in a case like this.  We've already got evidence that proves the building was fired I'm here to get all the story and I think you're the man who can tell it why me because you work for him I never worked for him never All right, we'll let that go for now, but you can tell me this Was Tony Midas the kind of man who'd start that fire? You can tell me if he really wasn't embezzler You can tell me if he tried to kill Arnold Bennett. I can't tell you anything for a fact dollar All I have is my own personal opinion. Well, that's what I want. I want that. I'd like your opinion Now there's something about Bennett's niece being married to Midas, isn't there a wife can't testify against her husband Everyone else in Bennett's office testified against Midas. She didn't I see Now the opinion. Oh Come on angle. Come on. You're right dollar. I have got ideas All of them make me sick inside Donnie Midas stood there and told me he was innocent He said it a million times if he said it once he said he thought Bennett was framing him to cover up from For income tax shortages. It's just so mys, but it fits Midas was a green kid hired into the company by Bennett He might have been hired to be framed on a phony embezzling charge that had give Bennett a good excuse on his taxes for a while I've I've been fooled a lot of times that Tony Midas fool. I don't know.  I tried dollar believe me. I tried to get him off Are you come here to me and say he's out of prison now and getting even he's burnt down a building and tried to murder Arnold Bennett Tony was a nice boy dollar, but now his whole life's gone and for what I Hope you don't find him or her I hope they go far away and stay away and don't have to talk to anybody ever they deserve that I hope nobody ever finds him But we did find Tony Midas He was right under our noses all the time When I got back to the hotel there was a message for me to get down to the county hospital Cord was waiting for me there They took us downstairs, and then we were both standing in a room looking at Tony Midas Before they took him across the hall to the morgue Funny thing Johnny There's been the alarm out on this guy for 36 hours Everybody's been looking everywhere for him and he turns up right here Only he's dead Yeah What killed him to you be he added awful bad.  He made them release him But he wound up here and died in this hospital just a kid Yeah Up until that time there had been some kind of a case against Tony Midas But obviously since he had been dead almost two days It was impossible to connect him with the attempt on Arnold Bennett's life and the firing of the building So we were right back where we started from trying to make a case against Arnold Bennett Who still lay in this hospital room and refused to talk to anybody who came near him All right, Johnny now what? And it's gonna be hard. I'm gonna have to work around him. His niece is the best opening I can think of. I don't worry. She police haven't located. Yeah, I'm not a trace Andy She had some information for me when she called last night. I still want to get it if I Got a break George Fully's in town Who's that best celluloid and Wickman in the country if you happen to want a building burned down One of the policemen at the hospital spotted him in the lobby trying to see Arnold Bennett Not a tired Johnny or is he now they followed him to an address on Beringo Street.  Hi Hey, uh, what's the story officer the way we see it fully still trying to get part of his money for burning the building He took a chance coming to the hospital tonight to see Bennett. No kidding. He'll probably make another try You boys have more at stake here than anybody if you want to talk to him try to make a deal with him to turn on Bennett Now's the time. What do you say Johnny? Oh, wait a minute. We aren't sure of anything about him He fills the bill Johnny Paraffin and wick jobs are few and far between with nothing more than that I'd stake my rep on fully being our boy for what it's worth all three witnesses now pick his picture instead of Midas Oh, well a good defense attorney get that thrown right out. What do you want to do? Oh? Let's shake him up a bit. Okay, go ahead.   Let's get out of here Here's our star to tell you about tomorrow's intriguing episode of this week's story tomorrow We have an arsonist right in the palm of our hands with very surprising results Join us won't you yours truly Johnny dollar yours truly Johnny dollar starring Bob Bailey is transcribed in Hollywood Written by John Dawson. It is produced and directed by Jack Johnstone Be sure to join us tomorrow night same time and station for the next exciting episode of yours truly Johnny dollar This is Roy Rowan speaking


While the city of Bemidji is bracing itself for another summer of heavy road construction, the largest project is along Division Street where three roundabouts will be installed over the next three months. Signs and markings for the project are currently all along the stretch of roadway. Construction will include three roundabouts, grading and by two minutes surfacing from the Highway 2 off-ramp by Bemidji High School and north to Wilton. Road closures will also be in place at the Bemidji High School entrance and a beside a road but will leave traffic open at Adams Avenue. Now the Adams intersection will stay open at all times. There are those that live south on Adams or the businesses in between the high school and beside intersections all will need to come in and out of the Adams intersection. So the businesses will be open, the people will be able to get in and out and so we need to maintain that one for traffic at all times. To help provide up-to-date information on the project, the county will be sending out email updates and we have information on how you can receive those updates at lptv.org slash news.


A very pleasant welcome to all my NPTEL viewers and participants. This is K. Punita Valli pursuing M. English literature in Congo Arts and Science College, Nanzinaguram Hero. On behalf of my colleagues, I convey my deep gratitude to my NPTEL management and coordinators and all the members of the NPTEL who has given a big opportunity to the learners to enrich their knowledge in various aims and subjects. I would also like to convey my deep gratitude to my department of English and my head journal, Ms. D. Anita, who has spontaneously supported me to try different subjects in relative to my literature. Yeah, of course, everybody has a motivator to do something better and best. I take this opportunity to share my experience, have about I have traveled along with the NPTEL only certification course, HMS, these many semesters. I have started my course on a great experiment in psychology, which has the most interesting subject and which is also the most interesting subject as I ever learned. On the next semester, I completed my course on cloud computing, which I thought it would be best for my placement. Yes, it was. By the following semester, I completed my course on outcome based pedagogic principles for effective teaching, which I felt very much interesting and very much useful for my teaching profession as I taught a course. In the next semester, I did my course on introduction to cultural studies and an additional course on visual perception and art, a survey across the culture. Both these courses are in depth in relative to my literature, which made me learn something beyond the world and beyond every syllabus, what I have already learned. In various aspects, they taught me a very big thing as well. What is your interest to do certification courses of NPTEL? I am already in a mood to do an additional degree first, but our government doesn't permit the students or the candidates who has done many cross major degrees to join in the work of government sectors so that I drop my plan. By the time, meanwhile, I come to know about NPTEL courses by my college faculties. So I thought of trying it instead of my degree courses, that's why. Do you have an objective in mind for why not learning NPTEL? Yeah, I have an objective in mind. I decide to learn different subjects apart from my majors so that I would get some idea about other fields. What are the advantages of doing NPTEL certification courses from your perspective? As I said before, the other subject or course which I prefer during an NPTEL would probably make me withstand in placements among different companies. Even after my UG, I was selected in various companies in regard with the NPTEL certificate and NPTEL course is what I have did there. Those credits was really plus for me in the placement cell. How do you manage your time for learning from the course? After my college, I accompanied myself with my tuition centre. After daily classes, I would continuously check my mains from NPTEL and look into my week notes and assignments. I prefer doing it during my weekends after my tuition classes, coaching classes with the students. How do you access the videos? Do you use college infrastructure for this? Yeah, of course. Sometimes I use my college infrastructure for submitting my assignments and sometimes I use my PC and my mobile phone for collecting or transcription materials and video lectures. How do you do the assignments? Do you take help from anyone? I usually do my assignments after reading the transcriptions. I don't seek help from any others. As these assignment questions are to entourage myself and also they support me during the final external exams. Have you used any other more platforms such as other courses via Coursera, edX, Pledge of Learn, etc? No, other platforms, I don't prefer it was very much good. I don't think those platforms was that much equal to NPTEL. I find a tremendous difference between NPTEL and other platforms. That is, I find NPTEL best a standard portal in comparison to other platforms. I wish I deserve to say that I don't utter that other platforms are worse but probably NPTEL was best than them and it was standard. That's it. Do you recommend NPTEL to your friends? Yeah, I have recommended NPTEL to my friends and from my institute, now at present, many were doing NPTEL courses and many were pursuing the exams best. Even I have suggested it to my sister and my family friends to do so. Any significant progress in your education, Karen? Because of course, doing such a great internship, getting a job, getting a better project, are all definitely, as I said before, I have gone to many internships and placed in various companies in our college campus interviews. Still I get to offer from various companies by citing my resumes with good certification courses which I did in NPTEL online exams. Any other suggestion from your side? Yeah, of course I have some suggestions to view to you. I would suggest if NPTEL can take any step to provide placement stage for those who take multiple courses more than semester. This is just a suggestion I wish to say here. And the other suggestions I wish to convey is NPTEL viewers. You can blindly trust NPTEL online certificate course as it helps us in various aspects to enrich ourselves to our educational field in this comparative world. Please do use the opportunity when it knocks once. That is, use the portal to learn more. This will be a best portal for those who are very much thirsting, acquiring knowledge on different fields. I feel an immense pleasure and a glad that sharing my fruitful experience with you. Thank you all.


We're now being joined by petroleum engineer, Zaka Bala. Mr. Zaka. Thank you. A very good evening to you and thank you for joining us tonight. Thank you and good evening to all our viewers across the globe. That's right. Now a single man in Nigeria is playing a very key major role in improving the economy of the country. 650,000 BPD of petroleum from a refinery is a lot. I believe you would agree with me on this. I totally agree with you, but like it's always the case. I mean, parents can have so many children, but it will just take one child to come up with initiatives and creativity that will bring that family out of the woods. Like parents will always appreciate it when their children are achieving milestones. It is the same way that government, political and economic leaders will appreciate it when corporate citizens achieve milestones. Dangote management or dangote refinery is a corporate citizen. It's a Nigerian corporate citizen. But what has happened today is something that is worthy of praise and emulation. Nigeria is happy about it. The West African sub-region is happy about it. The continent of Africa is happy about it. Then the global oil and gas community, especially the downstream sector is happy about it. So can we then say that the country is at Uhuru with the birth of the multi-billion dollars project, which of course is expected, as I said earlier, expected to produce 650,000 barrel of petroleum product per day, 650,000 compared to what the country has been passing by over the years. Is it now safe to say that this is Uhuru? Sure. What has happened is a positive development. The first thing that is going to happen is this. It is going to breach what we call refined petroleum product deficit, which we technically call distillates. Because with the quantity that will be refined, by the time you break the components, looking at the quantity of diesel, petrol, kerozin, aviation fuel that we are going to be seeing, then also looking at the products that are going to come from the petrochemical side. I mean, it's very clear that the economy is going to improve. But first what is going to happen is that host community where the refinery is, I mean, you are going to see some changes. There may be road constructions. There may be some form of corporate social responsibilities. There may be jobs that are going to be provided. But I want to also put something across. While we are all celebrating and appreciating what has happened to a corporate citizen, a corporate citizen of Nigeria, we should also remember to ask this basic question. Where did dangote management got the funds from? As far as I am concerned, they either got the funds from creditors or they got the funds from shareholders. So if they got the funds from creditors, we should expect that one way or the other, dangote must pay or dangote management or refinery must pay what we call cost of debt, which is interest and they will be expected to pay the debt. And if they got the funding from their shareholders, dangote must remain profitable to be able to pay dividend. So to that extent, we should expect that, yes, dangote has come on stream, but dangote has a private initiative is after profit maximization and cost minimization. And in the process, of course, they will recruit staff, they will take care of host community, but the primary objective is to maximize profit, to be able to remain afloat and minimize cost. Then once that happens, of course we all know, you cannot compare the distance that Nigeria takes to import refined petroleum products. In the case of dangote, I mean, it is near Rahom, so relatively it is going to be cheaper for Nigeria. And it is the same thing also for dangote refinery being within Nigeria, dangote refinery is closer to the source of supply. So now that the celebration of commissioning is over, industry watchers and stakeholders and other watchers will now wait for the practical crystallization of activities. So the next thing in the next one week, two weeks or one month, we will want to find out how in what manner will the crude oil be supplied to the refineries? Will it be through pipelines? Will it be through rail lines? Or will it be through tankers and the rest? If it is going to be through pipelines, the next question is, have the pipelines been constructed? And if the pipelines have not been constructed, when will they be constructed? If the supplies will also be through rail lines, the next question is, are the rail lines already on ground? If the rail lines are not on ground, when will they be constructed? And if the initial crude will be supplied to dangote using tankers, then there is every likelihood and it will not be out of place if we ask this question. That means the Lagos Ibadan Expressway and the other routes again that goes through Ekbe are likely to experience serious traffic congestion. And when you look at the load of the products, then there is every likelihood also that the roads, Lagos Ibadan Expressway and the Ekbe Expressway are likely to undergo some torture from loads. But the basic thing when you look at it, regardless of how you look at it, it's a win-win situation for Nigeria. It's a win-win situation for the West African subregion. It's the win-win situation for the African continent because there is what we call African unity core values. Remember, one of them is respect for diversity and teamwork. That's right. We must think Africa first. We must have, there must be integrity and impartiality. There must be efficiency and professionalism and they will need to share information and cross-fatalize it. Don't also forget that one of the objectives of the African continental free trade area agreement is to boost regional integration and boost the economy of the region and by extension the economy of the continent. So whichever way you look at it, it is positive for Nigeria, positive for the West African subregion and positive for the continent of Africa. And now while we're celebrating the positives and the key points you've mentioned about the transportation of the crude to the site, what we look at the crisis in managing a new outfit, so to speak, like the dangote refinery where of course when it's in full capacity it's going to give that 650,000 BPD, you understand? Now, looking at the old refineries that government after government have tried to resuscitate without success, what do you think the government should be looking at now even though we have this new refinery and there's so much confidence on it, wouldn't there be the crisis of abandoning the old refineries for the dangote refinery? No, dangote refinery is a private initiative. What I will do is this, I mean personally I want us to have more dangotes in health. We should have many dangotes in education. We should have many dangotes in agriculture. The good thing about having your corporate citizens doing well is because they will comply with the civil responsibility of paying taxes. I will expect the Nigerian government to support dangotes and their equivalents and more dangotes that will be coming behind and help them even if it is through joint partnership to construct rail lines and pipeline across the West Coast. What about the old refineries? What should the government be looking at now when the old refineries cannot be abandoned because there's a private refinery? I'm approaching that. First of all, I'm talking about how dangotes initiative will be supported. You encourage them, construct rail lines and pipeline across the West African subregion. Before you know, the depots in those countries will be Nigerian depots. We will have Nigerians working there, Nigerian accountants, Nigerian engineers and others working and that will give Nigeria strong economic power. Then back to the Nigerian refineries. Nigerian refineries are ten-old refineries or government-owned refineries. To the best of my knowledge, there are no moribond refineries in Saudi Arabia. There are no moribond refineries in Libya, in the days of Gaddafi. There are no comatose refineries in Malaysia, in Indonesia, in Iraq and Iran. To that extent, there is no justification and no reason why we should have moribond state-owned refineries in Nigeria. The only reason why some of those refineries are being moribond is because there has been leadership docility and we have allowed economic betrayals, economic marudas and economic vampires to pollute the point and the chain of crystallization of values for our country. But if Nigeria is a member of OPEG, like other OPEG member nations and they don't have issues of vandalization in their countries, they don't have issues of comatose refineries. I think in the interest of trying to grow and make sure we are a respected country, something should be done about it. Thank you so much. All that we need to do is to address the inefficiencies. Thank you so much, Mr. Balazaka. Thank you for your time.


My fellow Americans, there's been a great deal of talk lately about trade. Some of it's suggesting we should protect American companies from foreign competition. I don't believe the American people are afraid of competition. That was made clear to me when I visited a Harley-Davidson plant recently. It's a great story, and let me tell you about it. Not that long ago, it was being said that Harley-Davidson, America's preeminent manufacturer of motorcycles, couldn't keep up that the company was running out of gas and sputtering to a stop. Well, one of the worst mistakes anybody can make is to bet against Americans. At Harley-Davidson, the workers and management got together and decided not only to compete, but to win. With unity of purpose and a commitment to excellence, they cut the hours of work needed to make a motorcycle by one-third. Their inventory was reduced by two-thirds, and they tripled the number of defect-free machines they shipped. Productivity was improved, prices were kept under control. On some bikes, they even lowered prices. While doing all this, they expanded their product line from three models ten years ago to 24 this year. Today, Harley-Davidson is once again a leader in developing new motorcycle technology. They are now selling more and more bikes on virtually every continent of the earth. In fact, they are the only major motorcycle manufacturer in the world to have increased production last year. And yes, they have also increased exports. These Americans, confident in themselves and their product, have asked that their special tariff be removed so that they can meet their competition head-on. Current law provides companies like Harley-Davidson breathing room by applying temporary tariffs. Unlike some of the broad-sweeping protectionist legislation being bandied about in Washington, the idea is ultimately to increase trade between nations, not impede it. When you hear talk about a tough trade bill, remember that being tough on trade and commerce, the lifeblood of the economy, will have the worst possible consequences for the consumer and the American worker. First, it will drive up the price of much of what we buy. But worse than that, it could drag us into an economy destroying trade war. I'm old enough to remember the last time a so-called tough trade bill passed Congress. It was called Smoot-Hawley, and it helped give us, or at least deepened, the Great Depression of the 1930s. Well, the way up and out of the trade deficit is not protectionism, not bringing down the competition, but instead the answer lies in improving our products and increasing our exports. The government should work to create the conditions in which fair trade will flourish. We should be trying to foster the growth of two-way trade, not trying to put up roadblocks, to open foreign markets, not close our own. As I told them at the Harley-Davidson plant, it's time to gun the engines, not put on the brakes. Well, after visiting the Harley-Davidson people, it's hard not to have confidence that in the years ahead, America is going to be even more competitive, more aggressive, and more productive. We can meet the challenge and bring the trade deficit down. And the same is true of that other deficit we face. Each year, our government has been spending more than it receives in tax revenues, pushing up the federal debt. Some elected officials would solve the deficit spending problem simply by taking money out of your pockets via tax increases. Well, that would be unfair to you and could well knock the legs out from under our economy. I believe the more responsible course, the fairest approach, is to get federal spending under control. This year, total federal spending is projected to increase only 2.5 percent over 1986, well below the rate of inflation. The U.S. government will command less of the overall economy than it has since 1981, nearly a full percent of gross national product less than last year. But the budget and other legislation now being considered by the Democratic Congress threatens to undo the progress that we've made over the last six years. They would increase spending for a wide array of programs and raise taxes to reduce the deficit. Well, that's not my idea of deficit reduction, and I don't think it's yours either. This is not just an issue for Washington powerbrokers. It is something all of us should be concerned about. And I hope you will let your representatives know where you stand. Until next week, thanks for listening. God bless you. Cut. Two seconds, will you? Who am I making this out to? To. Once again? No. All right. Maybe it was for you. No, it's for him.


When you think of the Rocky Mountains, you probably think of Colorado or the Tetons, but the Wind River Range, seen just behind me, is quite possibly the quintessential mountain range of the Rockies. The Wind River Mountains, or the winds, as they are affectionately known, stretch approximately 100 miles in the western portion of Wyoming. These mountains are home to two national forests and three wilderness areas. They boast some of the highest summits with elevations over 13,000 feet and are also home to Gannett, Wyoming's tallest peak. They also have one of the largest glacial systems in the United States, including seven of the largest glaciers in the Rocky Mountains. The winds provide a mecca for backcountry enthusiasts. No motorized vehicle roads cross these mountains, but there are at least 600 miles of maintained trails that crisscross their way through the wilderness. So the next time you're looking for a unique mountain experience, think about visiting the winds in western Wyoming. If you won't be disappointed, from the University of Wyoming Extension, I'm Tina Russell, Exploring the Nature of Wyoming.


Hi everybody, this is Dave Vellante, this is theCUBE. We're here at HP Discover on the floor with Nick Vandersweep, who's the vice president of products and strategy and Cloud Cruiser, a relatively new company. Nick, welcome to theCUBE. It's great to be here. Yeah, so we first started talking, I think it was like 2010, 2011 at VMworld, you left HP to go to Cloud Cruiser. Tell us about Cloud Cruiser. Well, Cloud Cruiser is all about financial management, primarily focused at the cloud. And what we do is we keep track of all the costs that go into building out a cloud environment. And then we also build tenants for it. So we do the end revenue collection. So with a cloud, if you're a service provider, if you're not collecting usage-based revenue, you're setting up a VM and selling it at a dollar a day or a dollar an hour, we're doing all of the financial collections, the calculations of the rates. And then we also calculate the costs of all the, putting the service together, the facilities, the labor. And then we show the cloud provider the margin that they're getting on a per-tenant basis as well. They know where they're profitability on a per-tenant basis. We're all about intelligent financial management for cloud. Okay, so you can do this for a public cloud, for a private cloud, for an on-premise cloud, for cloud service providers. Yeah, so with a private cloud, it's a little bit different, right? What I've found in my history of working with cloud is if you install a private cloud and you give the keys to end users and developers to deploy services at will, they will, but they'll never turn anything off. You're actually, you're costing at least double compared to traditional IT, if you don't have a financial regulatory mechanism to say, oh, it's costing you a dollar a day, five dollars a day. They'll turn things off because they don't want to, because they want to stop paying and they're just going to hit their budget. And so it's a kind of an automatic way of regulating usage and driving up utilization. Yeah, it's kind of the old adage, if you can't measure it, you can't manage it. Now, last time I looked at it, Nick, I would say maybe 15% of the on-premise customers that I talked to were actually doing the charge management. That was probably around the time we first met with Q. Has that changed? Yeah, definitely. We're seeing a strong upswing with large customers putting in full charge back, and that's because at first, when those first cloud deployments went into play, they were more automation tools for IT. The IT guys were, instead of setting up all this infrastructure, they would have an automation tool, a cloud deployment tool like HP Cloud System to deploy infrastructure for service. The end users didn't get the self-service portal yet, it's still IT people. So the regulation was the IT people were taking care of it. Now, what's happening now is those cloud, those self-service portals, the IT people are handing it to developers, they're handing it to the end users. That's when you have to have charge back for your possible exponential. So can you talk about some of the customers that you have, whether it's on-premise or cloud? Sure. We've got customers, service providers like SHI and Exponential E and CMock, CMock's a Netherlands-based service provider, providing cloud services to customers in the Netherlands. We are their full billing engine for them to use space pricing, selling their services. They also, like I described for CMock, calculate costs, calculate revenue, and they can manage their business with specific target margins on a per-customer basis. Yeah, so SHI, we know SHI has been on the Cube, and so what do they typically do? If they don't use your product, they just build their own, is that... You know what's the most common competitor? Excel. People, especially service providers, they've been in this world of outsourcing or dedicated those things. They put a contract in place with an end customer and it's $5,000 a week, it's $10,000 a week, and they're moving into this usage-based pricing and they're doing their best that they can. One service provider that we worked with, we found, we come to them and they were taking 30 days from the end of the month before they could get an invoice into customers' hands. So that meant 30 days of delayed revenue, they installed Cloud Cruiser, we give them the invoice one second after midnight on the end of the month, and it's in the hands of the customer immediately. They get their money three days early. Cash flow, baby. It's about that. What also happened with them, and I've got multiple that happened this way, is they put the invoice in front of the customer, the customer disputes it, and they say, no, I don't think I use that capacity on that day, and the answer, would you take a 20% discount to a rule of trying to prove to you that you did that, and that's the normal way of resolving disputes, give you a 20% discount. And so again, with Cloud Cruiser, instant invoicing, and if there's any dispute, there's an online multi-tenant portal with our product, the customers log in. They can see their invoice, double click on the numbers, and it expands everything out. They can see what capacity they used on each day down to the minute, so there's no disputes going into it, it's like tracked all the way back to minute by minute. Awesome. We're sitting in the market at the right time, Nick, and Cloud is exploding, we've got a very strong value proposition for feeding against Excel, taking 30 days to get the billing if you're lucky, and so congratulations on getting the company going, it sounds like it's going really well. Doing very well, very, very well, and we've just been honored as being declared a cloud system-ready partner for HB, so we just integrate right into the HB cloud system environment, just push the button, and get it installed in less than any in the HB system. Hang on for the ride. All right, Nick, hey, great to see you again. Thanks very much for coming to the Cube, and thanks for watching everybody. We'll see you next time.


Alright, so this is just a little inkjet print project. I sprayed it with spray adhesive and I pushed out all the bubbles with a brayer. Did a quick trim along the edges. Now I measured the art resin. So it's half from the resin bottle, half from the hardener bottle. You just stir for about three minutes, pour it right onto your print. So it goes on like clear honey. I just rub the extra stuff into the side. If any little drips form, I just file them off. So I torch out the bubbles making sure it's perfect. Cover it and I'll be back in the morning. Sorry, that's all I got. Can't share anywhere other than that. Love to see you tuned.


I'm Rafael. I'm a software developer at IBM. I've been there for for a couple of years, I guess a bit of my background before IBM. I was a data scientist. I don't know if anyone's from Canada here, but I worked for a large grocery company, maybe they are under fire a lot in Canada, but there was a data scientist and I was basically doing data analysis and maybe modeling on customer behavior. They have their loyalty program and you can see kind of what products they're buying and doing things around maybe recommending products to certain customers in order to grow their baskets and so on. Really I think most of the time I was just looking at my own data and just seeing how many bananas I was buying on a weekly basis and not getting through them all. But something I learned there and I bring it up because they were maybe earlier in their data transformation journey, so they hired a ton of data scientists like myself and essentially we had a bunch of teams and everyone was working on different projects, but models were basically trained and kind of operationalized locally. So if somebody wanted my models results the next week and to input it into the recommender system to recommend a product to a customer, I would run it at the end of the week and on Monday hopefully the results are there and then I would send that spreadsheet to someone. So I bring that up because it's interesting that I ended up transitioning I guess to IBM where I was a software developer and within maybe the past few months I joined the open technology team who I speak for now and I've been dedicated to this project called Model Mesh. It's actually kserve slash model mesh and learn a bit more about that because it's all about model serving and so that's basically what I'll be talking about today. A quick outline on I guess what I'll touch on, I'll talk about model serving in general, give a bit of a background and maybe some of the considerations that one has to have. Then I'll introduce of course the projects kserve and model mesh. I'm not sure who has anybody heard of kserve or model mesh. Okay, so there's some familiarity. That's good. So basically an introduction I guess giving some background to those projects and how they're intertwined maybe a bit of their history. Then I'll dive a bit deeper into model mesh itself and give an example of how to monitor a model mesh deployment using Prometheus slash Grafana and you'll see a dashboard there too. And then at the end we'll talk about bringing in your own custom models into model mesh and I guess extending some of the integrations that we have there and how you would deploy a model and that's a bit of a demo there too. So model serving as most of us probably know is a very important part of the machine learning life cycle or of the pipeline and there's a lot more involved in it than at least I originally thought when I worked for a couple of years ago as a data scientist thinking that if I trained my model and I'm sending my results to someone I'm technically serving my model and that's great. But model serving is a lot more to it. It's basically this idea of deploying trained models in production similar to maybe an application or software and making it available to consumers or the application in order to actually incorporate that trained AI into the service that I'm deploying. So one of the most common model serving strategies out there right now are deploying this idea of deploying models as a microservice and there's a few reasons for it. One of the probably biggest reasons is just the familiarity that many software developers have in deploying their own software stacks their own existing software stacks in this style. And basically we can think of a model as an application where we want to make some kind of interface to the model and expose it via some kind of API to make it easy to inference upon or make requests to. But there are tons of considerations that come with this model serving strategy some of which are listed here like containerizing the model. How do we do that but there's also questions about cost and resource efficiency. You know our models overscaled or underscaled are we efficiently I guess providing the resources to each of those models. How do we handle ideas like rolling out or rollouts so you know if I want to update a model version and in machine learning there's a lot of experimentation so this happens often. How do I point this new service to this new model and how would I revert it if I realized that model isn't working. Can I put a model out there without directing live traffic to it. And then there's the idea of handling different frameworks and model formats you know there's there's TensorFlow PyTorch. There's Triton server ML server do we have to rewrite the interface for every model that we write if we want to deploy it using some other runtime and so on. And that's where the project case serve comes in. So case serve of course answers all of these questions and it's meant to be this sort of one stop shop platform that takes care of all of those responsibilities for you. It's built entirely on Kubernetes making it you know serverless and without vendor lock in and so on. But there's a big emphasis in case of for standardization as well, meaning that the run times or servers like ML server Triton server towards serve all actually use the standardized inference protocol that case or provides. Meaning that if I want to deploy a model on a different server case serve is is or they've already implemented that same inference protocol and there's no need to rewrite the interface or anything like that. Case or does a lot more than that too. There's also pluggable support for for ideas like like pre and post processing scripts monitoring not just at the ops level but also at the model level so things like drift detection or bias detection. And of course model explainability as well. So case or does a lot of these things and I don't know if anybody's familiar with kube flow. But k serve used to be under the umbrella of kube flow as known as kf serving. It used to be called kf serving and basically a couple years ago or so it was pulled out of kube flow and made into its own independent project renamed to take a serve. So that's just I guess a bit of history about the project as well. So one thing that case serve does or I guess the way that it's built is on this paradigm known as the pod per model paradigm. And so if I want to deploy a model that means that case serve is deploying a pod and inside that pod is the runtime as well. Maybe there are other pods associated with this one model as well. And you can start to imagine that if I want to deploy multiple models. That means more resources that means more pods are being deployed and so on. And then there's this trend in the field to deploy larger numbers of models for ideas like better accuracy and even to protect privacy better accuracy because you know if you think of maybe like a news classification service or something. You would want to model maybe per news topic as opposed to in order to narrow the scope as opposed to to making one big model that kind of does it all. And protecting privacy has to do with separating data and so the ability to train models by isolating data say you're you know where I used to work at the grocery giant. They wanted models at the user level and so in order to not have any data leakage or in order to protect privacy between the user data as well you kind of isolate them and train models on just that particular type of data. But again on this pod per model paradigm you start to think about the limitations that are somewhat straightforward like compute resource limitations. The maximum pod limitation it goes against you know or I guess you rub up against Kubernetes best practices when you start to think about thousands of models and that means thousands of pods and the same thing with IP address limitations because each of those pods need to be assigned to them. And so the answer to these limitations that's where model mesh comes in. And model mesh is interesting because it was actually in production at IBM for maybe the last 8 or 9 years now and it continues to serve as the backbone for a lot of Watson products such as Watson assistant Watson speech to text and so on. And it was created because in the case like Watson assistant if anyone's familiar with it it's essentially you can kind of create maybe like a chat bot for your store or for whatever it is and it's kind of a speaking agent.  Instead of making one very large language model you have just a few maybe kilobyte sized language models that that are just specific to the flower shop. So how would you actually orchestrate all of these models and so that's that's kind of the idea of model mesh and it's designed for these this specific use case like this high density or frequently changing model use case high density meaning thousands, tens of thousands hundreds of thousands of models and frequently changing meaning that maybe not all of these models are are popular used at this moment right there there might be certain times of the year where my flower shop language models are being requested upon often and other times not so much. And so model mesh intelligently loads and unloads the models to and from memory and that's that's kind of the main thing it does it can handle tens of thousands hundreds of thousands of models but not all of them are on memory. And basically model mesh is kind of the brain of the operation which is optimizing between responses that responsiveness to users and it's computational footprint and I'll go into a bit more detail about how it does that here. And so the main ideas of model mesh is this idea of cash management, essentially pods are managed as a distributed LRU cash, so you can think least recently used cash where models are loaded and unloaded based on two main variables the usage recency and the current request volumes. So you can think that a model which has not been used recently, in other words not inferenced upon recently, and has no request volume whatsoever would be simply taking up space and memory so we can unload that model, but then as maybe the volume rises or request volume rises for that model then we can load it back in and it can stay there. And then we get into intelligent placement and loading, which is talking about the idea of model mesh being able to scale out models across pods as well so if a model has a large request volume it can make several copies of the models to have that model to handle those requests. And then there's other things baked in like resiliency and simplicity, the idea that failed model loads are automatically retried in different pods and rolling updates right I kind of mentioned rollouts are handled automatically so there's this idea of an inference service in case of a model mesh which is essentially the mapping from a model to the pod and that inference service is mapped to the model or something called the V model and if you wanted to update that model or create a new version model mesh will make sure that that new version of the model is completely loaded before the inference service will actually direct a new traffic to that model. So this is an overview of model meshes architecture. There's a few things obviously to look at here. So for every model that I deploy or every. Yeah, so for every model that I deploy there would be a runtime deployment and that's what you can see in this light gray. Imagine that this runtime X deployment is like a torch serve deployment and runtime Y might be Triton server and each of those deployments have a couple of pods and that's that's just the default number that would come with it and the letters inside of the the container the model server container are our models and you can see that there's copies across pods maybe B and C are more heavily used and then AD and E on the left there. And then there's a couple of other sidecar containers for example the polar which is specific to the type of runtime or the type of model that you're deploying and that's the logic of you know pulling the model from storage and into the pod. And then we have the mesh sidecar which is the kind of brains of the operation doing the routing the caching and the placement of models. You can also see on the right model mesh brings in its own at CDN since which contains metadata about the models and that's where it would be getting information like like like model sizes or model IDs and so on. So that's a bit about case serve and model mesh and now I'll show a bit of an example I guess of monitoring a model mesh instance and what that might look like. And here in general is just some information about why we want to monitor the state of resources and usage especially when it comes to a high density model serving environment. You know and this isn't necessarily monitoring the model itself or the accuracy of the model or you know it's drift or anything like that but more on the upside so things like cluster utilization and model sizes and model loading times. And of course by monitoring this metrics one one can react to and make changes to the resources if necessary. Especially when you're talking about hundreds of thousands of models usually it's not just one model that's failing if if you see models that are failing it's probably an entire type of model and it's multiple and so that would need debugging and so on. And so model mesh comes with metrics that are exposed via an HDTV endpoint and here are just some high level instructions on how how one would would set up maybe their Grafana dashboard and take advantage of these metrics that model mesh is and that would be using Prometheus the open source monitoring framework to monitor the the namespace in which model mesh is deployed. You would create a service monitor which is a resource that Prometheus provides which is simply scraping the metrics at the endpoint the model mesh is exposing and then you would access Grafana which is usually used hand with Prometheus but essentially a visualization tool and model meshes repository actually comes with this this out of the box chase on template for for a dashboard. And here I have an example of what that might look like. I think that's probably visible and yeah so so this is just the first half and there's actually more updates coming on this dashboard. I think within the next few weeks it would be released but I'll talk a bit about that but at a glance you can see things like like cluster capacity and utilization at the top. The number of pods and and on the left side. Second from the top you see model counts and I guess this is interesting because we can see in green that 60,000 models are actually managed which just means that there's 60,000 models that model mesh is aware of and they're registered and but the number of models that are actually loaded into memory is maybe closer to to 5000 or so. And that's the idea right that there are 60,000 models that are ready to be inferenced upon and as soon as I want to model mesh will load in a model if it hasn't already been loaded into memory. And on the right that messy graph is just the sort of dynamic nature of the of models moving or being loaded in and out of pods over a few days. And and then the bottom half of this this dashboard is essentially inference inference request rates and response sizes and so on. And the second half of this dashboard we see some more inference inference API requests or response information but in the middle we see the cashmiss rate and this one's interesting because this is capturing maybe the delay between a model that isn't loaded into memory so one of those managed models and maybe if I make an inference on one of those that would be considered a cashmiss and we can see that maybe the slowest one took about close to 1.5 seconds to actually be loaded into memory and then and then inferenced or or response sent back on the bottom left of course we see the model loads and unloads over time we see sizes and loading times and yeah in the next few weeks there would be in addition to this to this dashboard at least the out of the box dashboard because the graph on is completely customizable so you'd be able to make it the way you want it to be but we we added in the partitioning I guess by serving runtime so if you wanted to just focus on looking at only say your torch serve resources so however much resource you've dedicated to torch serve or ML server or Triton server you would be able to see all of these partitioned by by those serving runtimes. Which pulls me into serving runtimes in general which is essentially the template for pods that serves one or more model formats and right now model mesh supports out of the box. These following four we have Triton ML server over a mass and torch serve but there always exists these cases where the out of the box integrations aren't enough. So for example the framework might not yet be supported maybe you have a custom model that isn't you know an easy like S K learn model or actually boost or something like that that you can easily deploy on on one of those those integrations. Maybe you have some custom logic for inferencing and so that's kind of the nature of K server model mesh is this idea that CRD is allowed for extensibility and you don't ever have to touch the model mesh control or code or anything like that. And it's especially easy of the desired custom runtime uses or is written in Python because ML server can be used to kind of provide the serving interface so you can take your custom model and and kind of use ML servers interface and that'll be the glue between your custom model and model mesh. Which is actually the example that I go into a bit more here. So again some high level instructions you would obviously prepare your your model and interface it with with ML server which I'll show you in a second. You would containerize that that into your own custom image, which would include the model and all of its dependencies to run. And then you create that new custom sorry that new serving runtime resource and and serving runtimes by the way in at least the latest release of model mesh can be either cluster scoped or namespace scoped. Meaning that the cluster scope serving runtime of course is is available to the entire cluster so if you have multiple model mesh deployments they would all have access to that serving runtime and the namespace scoped would be scoped only to to a specific namespace. But here's an example of what I consider I guess a an implementation of a custom Python model. I have a model called the tourism activities model. And here I'm implementing ML servers ML model class and that's just the fact that this is a Python based model it's the easiest way to to get this custom model into model mesh. And all you have to do is work on the or sorry implement the load and predict functions the load function obviously loading my model from wherever it's saved and then the predict function doing the inferencing and sending out the response however it needs to. I will say that my model is really no model at all it's simply a JSON parser. So if I send it any JSON it'll spit out what it's parsing but it'll also throw in some some maybe hard coded information out as well. So here we have our custom runtime definition and. And we can see that the the custom runtime name is the tourism runtime and I call it this simply because this custom runtime of mine I only wanted to run tourism models. This could be you know an ML server runtime a torch serve runtime but my my tourism runtime is is is the way I'm naming it and then we have the supported model formats here. This is important because this is how model mesh will know which run times to use when deploying a model so here my tourism runtime is supporting only the model formats tourism models so it only takes tourism models. This could be also you know SK learn or or or something like that. And then of course we're we're we're pointing it to the custom container image that I created prior which holds my model and all of its dependencies and then there's there's room for environment variables depending on the runtime if they require it. And then when it comes to deploying the model the model of course has to be on some shared storage and we would create something called an inference service to serve the model. I think I mentioned a bit about the inference service before but the inference service is basically the mapping from the where the model actually resides into the pod itself. And it represents as it says here the model's logical endpoint for serving inferences and then we would of course check this the status and I'll show a bit of a demo I guess of what that might look like. And and here's our our inference service so we have the model name I named it the tourism activities recommender and we have the model format the tourism model and again this is important because this is the way model mesh would know. Oh this this format is a tourism model so I have to go look at the available run times and see which ones support that. In this case though we have this option of spec which is me explicitly telling model mesh that I only want this this inference service deployed on the tourism run times. And this is optional because model mesh is smart enough to look for which which which run times support this model format and of course we're pointing to where the the model is actually located. So with that I can pull up a terminal and I can kind of do. A lot of this live just to see what that looks like. Yes. Yeah. Okay so here I have a model mesh serving instance already deployed and we can see our three main pods we have the controller local Minio which is where my models are sitting in the at CD that I mentioned before holding metadata about any of the models that are being managed. If I look at my serving run times. We can see I actually have four available to me. We have this OVMS one time we have the model type. This is pointing to like or explicitly telling you which model formats it they support so we can see like our Triton runtime supports these Keras model types and then which containers they're built upon so we can see that my tourism run time as expected is built on this ML server container. And if we take our our inference service now and I'll just quickly show you that this inference service is exactly I guess actually as as I showed in the in the slides. But if we deploy it we will see that two pods of spun up and we have this tourism run time pod and the spun up and they're running pretty quickly but sometimes of course it takes some time to create it. And what we would see when we look at our inference service in this case we can see that it's actually ready and it has its endpoint. But in another case where maybe the pods take more time to spin up we could have seen that of course the ready would be set to false and if you were to describe this inference service you it would tell you the reason for example that waiting for Todd pods to spin up I might say failed to load the model maybe you couldn't find it and so on. But in this case our pod spun up very quickly again to is the default and we have this inference service pointing to my model and storage ready to be inferenced upon so I will just put forward it and then here. I have a script which is simply making a gRPC request sending some payload some JSON to the model and and getting its response here printing it out so here's just some data metadata about the the request itself and and this is kind of what this again this trivial model does but basically it spits out the payload that it received which is here this this name Vancouver activities the message where should I visit in where should I visit in Vancouver maybe that could have been grammatically a bit better but. Before it said what should I visit in Vancouver so I really wasn't thinking and then the server response which is just telling me some few places that I can visit in Vancouver like Stanley Park in Vancouver aquarium so again this model of mine is simply a JSON parser and then outputting this this this hard coded response but I just wanted to show how to take a Python based model a custom one that isn't trained you know using some library. That's already supported by one of my run times how I merged it into ML server and created my own container for it and then ultimately. Deployed it on model mesh and just like that it's it's ready to serve. So. Let's see. Okay. So yeah that's just what the script looks like and then here yeah just some links and contact information so model mesh is always looking for contributors obviously it's a growing project case serve as well there's there's lots to do on both sides and we have a biweekly call. Which you can find more information on in the case serve channel and it's still under the cube flow I guess slack workspace but in that case serve channel is where you can talk about case serve and model mesh. And we have this this monitoring and performance testing repository which is interesting if you're more interested in some scripts that will actually like stress test model mesh and its capabilities and easily deploy. You know tens of thousands of of small models that would be the place to check out and of course we have links to more about the customer on times in the Grafana dashboard and my contact information I threw up my colleague Christian who who also works on model mesh with me at IBM. In case you don't like me and you'd rather reach out to him it doesn't matter. And and yeah that kind of concludes everything I wanted to talk about so thanks for listening. Okay. If you have a question I got to make you just be clearly for the people battle. Thanks for the talk. I had a question about the sidecars that I was wondering why the polar was separate from the mesh sidecars that make the polar a pluggable or is there some other architectural reason. Yeah I mean that's that's definitely I guess an architectural design question that's that predates me but I know that the polar has. It's specific to the type of runtime and I guess your question is why why are there maybe multiple of them in the pods or why why it's not like just embedded into the model mesh controller itself. That's probably the idea of keeping them separate I guess the polar has code that is specific to the models server itself so like torch serve is expecting a specific way to get a model and to make that model available and so on. While the mesh is kind of I guess a separate entity and trying to be not I guess merged with with that type of code. Yeah I mean I can connect you to the architect himself or you was it red hat or no. Yes yes okay well I know we probably have each other on Slack so if you're you're more interested I can connect you with the person who designed this at IBM. But but yeah that's all I got. No thank you. Hi thanks for the talk so I'm wondering like what's the biggest use case that you use model mesh for like how many concurrency for the wallows how many models and the total like data model size and also how many nodes involved. I mean I don't I definitely don't have all those specific numbers but this is this this temp this like is just a sub component of some. Watson service in production and just here they have 60,000 models. It was built for hundreds of thousands of models I know on this if you're really interested in specific numbers like even in this model mesh performance repository there's some statistics of just how much it can scale up. If you want me even after we can open it up together and I can point you to where that information is but but yeah like to give you an idea this is a very small component of Watson and it's already managing 60,000 models so you can imagine that there's there's there's a lot more. But yeah. Okay thanks and there's another question for the model versioning so basically like if you frequently update the model how can the model mesh measure the application they load the same version of the models. Sorry what was the last part how they can how can they like make sure that all the applications they load the same version of the model. So like I did the model today and then how can I make sure other applications they use the make the version A instead of version B. Well I guess that's this idea of the V model which is again like the inference services mapped to the version of the model and the inference service won't actually point to the new model until it's completely loaded and ready to go. So I don't know exactly. So just like model serving cluster they have maybe dozens of nodes and they maybe are already loading the model so you mean that maybe from server side they already know that they need to update the model at a time. Instead of when they are updating the model they could update the old model at the same time when you are loading them updating the new model. So I'm wondering would there be a model versioning thing. I'm not sure if I understand completely but basically like at least from what I understand the idea is that the the inference service won't be changing any models until that new model is actually loaded. And so the idea there is that that traffic is only pointed to that to that that already deployed and current model before before moving to that that new model. So basically decide on the business side. Yeah I guess yeah I guess and I don't know if it's something maybe that model mesh doesn't have and K serve might have that feature because I know maybe you're touching on this idea of like even like the multi arm banded approach or or canary rollouts and things like that like those more advanced type of features. I know K serve has and we're working to bring into model mesh. But yeah. Thank you.


What's their name, dude? It's going on, everyone. Sam here, playing sports cards, ripping 23 tops for Steve baseball, hobby, eight box, full case break, number 10, random division style. Here's a look again at your random results. As always, you can find those on the website under the Results tab, as well as the link in chat. Ramar is Aloha. Ramar is what up, buddy? Here we go. Oh, hello. Isaac with the Luminance spot. Sean with the Luminance spot. Oh man, we're down to two to go on Luminance now. Two to go on Luminance random team. 10 to go on NTPYT. Teams are on sale. Let's get the rest of those squads sold out, everybody. Five to go on NT division, full case random divisions. Going to be awesome. And 15 to go on Star Wars. Sig series, random hit style. Tops echelon. Tops echelon was good. I like tops echelon. You know what they need to bring back? Tops minted, I think it was called. There was inserts. They were kind of like dare to tears. And some of them had like money. Like 20 bucks. I think I've seen that. I've seen that on YouTube. What? Yeah, tops minted, I think they were called. Someone ripped a Gilbert Arenas one. There's 20 bucks in it. I was like, all right. I don't think it matters. I know what happened over here. It's all like the knife sliding. All right, good luck, guys. Here we go. Good luck, everybody. Let's do it. Had a banger last case. Orange, Corbin, Carol, Pink, Volpe to 15. And a red Gunner Henderson to five. He's rolled better on. Hey, what up, Mooc? Mooc. Wow, that's a good box. Already a good box. Evening cam. Cam and card sorter guy, yellow. Yellow on Mooc. Yellow on Mooc. Card sorter guy with that name too. That's Vladimir Guerrero, senior Marcus Simeon. Going, going, gone. Adrian Beltry, AL West. What up, Mooc? What up? What up? Hello. Louisa Rise, Poiple, Ty France, AL West. 99. Logan O'Hoppy, Rookie. Oh, snap, J-Rod. Oswald Parazza, Refractor, and Tim Anderson to 50.  What? Say I'm getting choked up, I don't know if I'm finished. Me or Kyle? Me, Kyle, I'm sorry. Probably, yeah. Yeah, Babe Ruth. Let's look at it like this. Scratch it, twist it. You have the tie wrap. J-Rod and Tony Gonsolin. Hey, guys, such a bot. There's Francisco Alvarez going, going, gone. Louisa Rise, Poiple, Aaron Nola to 99, and El East. Man, there's been some good clips on... Oh, God. I gotta work to get better. I guess I'll do 30 seconds. Kodai Senga, probably, yeah. That's probably the way to do it. Got a Juan Soto and Tony Gonsolin, and a West. Jersey Auto. No, no, we haven't stopped. I don't think we're planning on stopping. Brett Beatty. Alejandro Kirk, or Al East. Salvador Perez, Al Central. Blue, Bryce Harper, Al East, 275. Got a nice one here. Pink Sammy Sosa, Al Central, nine to 15. But a base auto, Anthony Volpe. Going to the Al East, nice one there. Incased on card. All that good stuff. Greg with Aluminum Spot. Greg. And more stuff, gotta believe, I could say thank you. And NL West, man named Machado, 72.99 on the auto. Machado auto. Congrats on that. NL West, going to Joe M. That's Joe. Joe. All right, let me go, I'm gonna update real quick and just see what, if anything else, I think. The luminance sold out. Yeah, luminance sold out, 10, 5, 15. That's all we got left. Three breaks at the end of the night. At P with some personals. I didn't see Remarz, did he do well on his first game in triple? Triple A, that's big. That's actually pretty big. Okay, keep it going, on to the next. 10 teams remain. Yeah, it's straight fire, it is quite the adventure. Opening up for Steam, quite the adventure. Quite the workout. Quite the workout. Teams are all marked down in NT as well, by the way guys. All marked on down.  Yeah, that's a little overkill. T-swag to 75, NL Central, Nolan Gorman Rookie. A going, going on, Aaron Judge. I'll see you super in this last case. Michael Stafanik and the Lindor, three for six, double, three RBS. That's pretty solid actually. Dominguez in triple A.  Ripgen and Kerry Carpenter, refractor Rookie. Should be. I mean, Cashman needs to go, that's the issue. Corbin Carroll and Shohei, because it all starts from the top. Shohei going, going, going to the A.L. West. Zack Blesack, Purple Jose Trevino to 99, A.L. East. Michael Harris Rookie and David Ortiz. Yeah, that's what I'm saying, honestly enough. Cashman too, he might as well. I'm good if both of them get fired, I'm happy. Vinnie Pascantino, refractor Rookie. Blue O'Neill Cruz to 75, nice one there. The Mets are closer to the wild card than the Yankees.  Nelson Bolasquez, Michael Harris Rookie.  Ty France, there's Kyle Schwerber, Jersey Auto around the Diamond, and A.L. East, nice one there. That's what I'm saying, right? That's what I'm saying, dude. Longest losing streak, nine games since 1982. And those 80s Yankees teams were terrible. Like if you're getting into comparisons with those Yankees teams, Nolan Gorman, Fresh Faces, that is bad, that's pathetic, dude.  I know you do. Absolutely love it.  Fair. Yes. Christy Ellis, well put, Vaughn. Nice one here, A.L. East, Gold, Aaron Judge, 35, a 50 for the A.L. East. Sam, I think you hit a Gold, Vladdy Jr. to 50 in the last case, I believe. That is a Corey Seeger, two, 15. You are in the last P.U.I.T. Oh, what did I say? What did I say? What Merrifield? Base? Oh, maybe Rich is talking about what? Cause he's a J in this, I think, right? Okay, that makes sense. One of 15. Max Castillo, A.L. Central, Rookie Auto. No, you're good. Yeah, this is the division's case. You are in the last one, I'm pretty sure. Sam with the P.U.I.T. And Blyamadris, Refractor, Rookie Auto, A.L. West. Blyamadris. Hey, let's go, Logan. Yeah, Logan, you're doing well, man, with the A.L. East. That's, the judge is pretty sick. You hit the Volpe, too. Volpe was pretty big. Volpe works, for sure. Three breaks remain on the night, everybody. That is it. That is all that remains. That's awesome. Thank you all. Appreciate that. Again, if you guys are interested in personals, I would order sooner rather than later. Tyler's getting slammed right now with personals. Luckily, keeping him busy, so he doesn't bug us. That's a big positive. 10 to go on NTP.I.T., everybody. National Treasures Football. We are over two-thirds of the way sold out. That's awesome. Thank you all. In general, you win a Blue Ted Williams, A.L. East. Won an A-Rod. That'd be pretty sweet. That would be sweet. Oh, snap. Bryce Harper. Now, no one will ever be over that. Sticks are here to stay, my man. Buddy Kennedy and Josh Smith. I don't know if it's a Yankee. That's the only thing. That's the only thing, Logan. I can, let me look real quick. I have the checklist here. I think he's only a Ranger. Ranger and Mariner, I believe. Yeah, he's Ranger in the auto. Ranger in the auto, Jersey auto. Yeah, no A-Rod Yankee auto. It's all James with the rams down to nine. Nine to go, everybody. Top's kidding. I don't know, to be honest, Mooc. I really don't. A-Rod. It's gonna be, there's a lot of just unknown at this point, that's the thing, you know? These are, Harrison, you make a good point. These are very similar to Prism Salos. Clemente and McCutcheon, both for the NL Central. I'm sure Top's is moving. Oh, yeah. I'm sure they're firing on all cylinders. Which is all I have to do. Tatsis and a gold miggy, A-L Central to 50. He goes, Logan goes, we'll never mind. Got gunner and Adley potentials though, which is good. That is a going, going, gone schwarber and a going, going, gone of Mark McGuire. I think we missed the insert in the last packs. We had two, that one's thick. I'm gonna do that one last. Josh Naylor, purple, Larry Walker, and a Central to 99. Jordan Walker, rookie. Bobby Witt, fresh faces. That's a super fractor. That's a super fractor. Tom Glavin, N-L-E-S, that's nasty. Let's go.  Oh, that's so sick. Hell yeah, man. Going to Nicholas I, congrats, man. Dude, that's so freaking cool. Uno de uno. Ooh, let's go. I can't stand that one, I think. That's dirty. Snell, what up, Snell? Snell. Alcantra, Josh Young, fresh faces. Let's go, man. Let's go, dude, that's so awesome. I love Tom Glavin too. Oh, that's so sick. All right, we've got Orange, Sammy Sosa to 25 NL Central. Michael Stefanik to 99 on the Rookie Auto, A.L. West. I'm gonna send an empty box and wrap with the A.L. West. You can see that's out of the mag. A.L. West, gonna get an empty box and wrap up. Chillin', man, chillin'. Yeah, same old here. Talkin' about how bad the Yankees are. To 75 is Bryce Harper, NL East. How bad the same sticks are. Trish and Casas, A.L. East, nice one there. It's been pretty good, man. Been pretty good, I have to say. This came out at a good time, Snell. Alec Minoa, A.L. East, and Joe Musgrove, N.L. West. That pristine is lookin' fire, man. Houston's put it on the socks, fair, fair. Umpkins, Larry Walker and Miguel Vargas for Fractal Rookie. NL West, there. There's Adrian Beltre, A.L. West. Shhh, yeah, Fleer's comin' back from the dead. There's Spencer Troglison, Blue Miguel Cabrera to 75, A.L. Central. Nice blue of Alex Rodriguez, A.L. West to 75. And Jordon, A.L. West as well. Max Kepler and Nolan Gorman, Refractal Rookie. Holmes, Gunnar Henderson, Fresh Faces. Yeah, I can legitimately, I can look at this card all day. This thing is awesome, that's so cool. Vaughn Grissom, Refractor. Purple Ironman, Cal Ripken to 99. The Cal Ripken to 99. Servin' Roblese, oh, Snap. Mookie Butts at the L9. Cots A, Jake Cronenworth, and Corbin Burns around the Diamond, N.L. Central, Jersey Aqua, nice one there. Corbin, Jordon Walker, Rookie. Chopper Jones, going, going, going. It's like scoreboard. It rose up from the dirt. Mark Race and Orange, Brian Servin to 25. Nice one, N.L. West. Josh Young, J. Ram and Vaughn Grissom. Yeah, I think it's a mixture of boon and I think it's a mixture of like, Cashman's made some very questionable moves the last few years, like very questionable. Zeke Otovar, that is a Brian Servin Rookie. Blue, Ron Vecunia to 75, N.L. East. N.L. East, again, Cade Cavalli, Pink Rookie, 15 of 15. Germain Palacios, nice one there for the Tigers. A.L. Central, Rookie Otto. And Leo Verpeguero, 84 of 99, N.L. Central, Rookie Otto. Nice ones there, keep it moving everybody. Aaron Judge, Grand Slim. Let's go. Are the Yankees back? No. Is that all it took for the Yankees to be back for you during the Yankees turnaround of the 2023 season? That's what I'm saying, Matt, honestly. No, no Ellie in this one, Perb. He won't be in Topps products like this until next year, most likely. Yeah, no Ellie, unfortunately. We'll write a book about this one. Oh boy. Good God, man. Berrios, Blue, Brian, Serban to 75. Nine to go, everybody on NTPYT, we're under the single digits, Javi Baez. Oh, Snap, Shohei Otani, Alex Bregman, and Randy Johnson. I don't think we choked out with Kyle's monster. Yeah.  Juan Soto, going, going, Juan. That's great. Corbin Carroll, I would say. Adley Rushman, Gunner Henderson, Josh Young, Yoshida, Volpe, Michael Harris, Jordan Walker. There's a lot of big names in this set. Miguel Cabrera and Jose Altuve. I would say Carroll's probably the best. Wheel of Nonsense. Frank Thomas. Yes, Cole can opt out next year. That's not good. Brandon Woodruff, Gold, Jake Cronenworth, and Al West. The Cronen Zone. Gold, going, going, gone. Shohei Otani, Al West. 46-50. And Sammy Sosa. Santiago Espinal. There's Jeremy Pena around the Diamond Jersey Auto. Nice one there for the Astros, Al West. Very cool. Jeter Down, Shay Langlears, Masatake, Yoshida, Fresh Faces. Rafi Devers, Purple, Josh Young to 99. For the Rangers, Al West. Salvi, Gunnar Henderson, Rookie, Fresh Faces. Yeah, Pina's Auto's nice. Charlie Blackman, N.O. West. N.O. West again, Randy Johnson that time. Got a Gold, Tyler Freeman, Rookie to 50, 27 to 50 for the, for the A.L. Central. N.L. Central, Tyler O'Neill, 40 of 75 on the blue. I got an Orange, Auto, Caleb Killian, 23 to 25, N.L. Central on the Rookie. Nice one there. And Cody Clemens, N.O. East. Orange, Auto, 13 to 25.  You know who else? But still, becoming an even bigger bot. Yeah, he's pretty bad. Good ol' Moe. Yeah. Yeah. Because we're not doing this. And what do we have? Nine to go on NT? Yep, yep, nine to go on NT, everybody. Yeah, that would be pretty sweet. That'd be pretty sweet, Logan. Nine to go on NT, everybody. Nine to go. Teams are all on sale. NT PYT, we've got LKLine and Evan Lee. Heaven Lee, Evan Lee right there for the NLE. Francisco Alvarez, Rookie. Guero. And Jordon, going, going, gone. Orange, Manny Machado for the NLE to 25. And Ginrayu, blue. Alvarez, two to 75 on the Fresh Faces. Nice, Francisco for the NLE East. Fran, Cisco Alvarez. Trish and Casas for Fractor. Purple to 99, Jill Maurer. Maurer-hour to 99. Veritech, you Darvish. Oh, snap, Ronald Acunha Jr. NLE East. Lubab and Barry Larkin on their fractors. Rookie, Adrian Beltraig, going, going, gone. Ben and Tendi. Purple, Michael Stafanik to 99 for the Angels. Aronado, there's a Francisco Alvarez. Nice one, Nick. NLE East, we've got gold coming up of Tom Clavin. We're going for the rainbow, everybody. Super and gold, congrats NLE East, Nick. Here you go, buddy. Got a couple more of those to get and you'll have the entire rainbow.  I mean, how hard can it be when you start with the super? Yeah. You know? Going, going, going, Albert Pujols. And a Christopher Morel on the roof. Ernie Banks, NLE Central. NLE Central. Blue, NLE East, Chipper Jones to 75. Gold, Shane McClanahan, ALE East. Going to send that empty box and wrapper, ALE East. I'm going to get one for the NLE Central as well. I think they had a damage card earlier. Point out. Yep, ALE East on this one. And then we'll do NLE Central on another one. NLE East, JJ Bleday, nice one there. Rookie Auto. And NLE East again, Cody Clavin's sweeping the autos on the end there. Nice one there, congrats NLE East. Maddox and Musgrove. Now I think he was at one point, yeah. My trout. I think you're right on that one, Matt. I think he's, I think he's in Chrome, in regular top scrum. He's a tiger still. Travis Swaggerty, purple to 99. NLE Central, I believe at least. I might be wrong on that, but. Hobby Baez, going, going, going. Yordon Alvarez, JT Riamuto. Yordon Alvarez to 75. A Oh snap, Nolan Arnato. Garrett Hill and Jose Canseco. Corbin Carroll, Rookie. And Kyle Schwerber. Spencer would know that, if Spencer was here, he would know how Cody Clavin's got traded to the Phillies. Ronald Acunha, Gold Babe Ruth, that's sick. 41 to 50 for the Yankees, NLE East. Yvonne Herrera, Francisco Alvarez. Fresh Faces, NLE East. Got a J-Ram and Walker Bueller to 99 around the diamond. NLE West, a lot of Walker Bueller's in there. It's like the first one I think in four cases. Jeez, it's three of these, JJ Bladet, Fresh Faces. Gregory Soto, Cody Clavin's to the Phillies for Matt Birling and Nick Matone. Wow, what a trade right there. What a league, league shattering trade. Jose Berrios, nice one there, NLE, or AL East. Anel Central, Alec Burleson Rookie. Nolan Jones to 75, NLE West. Orange, Joe Musgrove to 25, 16 to 25, NLE West. Got a Blimadris, or Blimadris, 57 of 99, Ale West. And Cody Clavin's, NLE East, 18 of 25, a lot of him too.  Divvy still at five. We're down to one, two, three, four, five, six, seven, eight to go everybody, eight to go. Eight to go on NT, thank you so much. Star Wars still at 15, eight left on NTPYT. A lot of the big teams are off the board already, so there's a lot of good value in there, everybody. A lot of good value, all teams are on sale. Divisions will be breaking after, that is a full case of NT, you get four teams, random division style. A lot of big hits to be had there, of course as always, empty. Goldy, Blue Lubeb to 75. Sam Otto to yellow card, I don't think they'd want that. I don't think they'd want that. Michael Harris, oh snap, what'd you say in that one? No, I'm just saying, I must have hit every single one to have sent them all. Shipper Jones just absolutely ruined the chat. Yep, what am I gonna do? Hi, Cobb and Matthew Batten, Sam's breaker used that, god.  Ernie Banks, Gold, Bryce Harper, at least. That was a Jordan Walker rookie. Yeah, I was blind-hass and then some students went through and some of the air got it. Babe Ruth to 75, nice one there. Mike Piazza, Corey Seeger, Purple, Barry Larkin, nice one there, two 99 for the Reds, NL Central, Owen Gorman rookie, Riley Green rookie, that is a William Contreras and a Patrick Wisdom around the Diamond Jersey Auto, NL Central. Nice one there, picked tens of dollars. That is a Michael Harris, Fresh Faces, NL East, A.L. West, Jose Altuve, A.L. West, Ken Griffey Jr., NL Central, Johnny Bench to 75 on the blue. Gold, NL East, Peter Lanzo, 50 of 50, last one stamped. Got Bam Bam J.Ram, A.L. Central, Jose Ramirez, nice one there. And Gold, Tristan Casas, A.L. East, nice one there, Logan, 45 of 50, congrats buddy, very solid hit. Do a quick recap here guys. We had Patrick Wisdom, Walker Bueller, Gold Tom Glavin, Jeremy Pena, Corbin Burns, Kyle Schwarber, Tony Gonsolin, Jose Ramirez, Tristan Casas, Gold to 50, Cody Clemens, Blyam Adris, Cody Clemens, J.J. Bladet, Orange Cody Clemens, Orange Caleb Killian, Lee Over McGuero, Jermaine Palacios, and Casas is pretty solid actually. Tristan Casas speaking of, Michael Stafanik, Blyam Adris, that is Matt Castillo, Manny Machado to 99, Anthony Volpe, Rookie Auto, and a Super Fractor, one of one around the Diamond Patch Auto, Tom Glavin, N.L. East. That'll do it for the break guys. Thanks again for the filp. Up next is the mixer.


I'm Julie Vos, Head of Digital Education and Deputy Director of Learning, Enhancement and Development at City University of London. I've worked in the learning technology sector for 20 years and have been an active leader in all of my roles. Since starting at City, I have led successful projects on digital accessibility and digital literacies and have been a driving force on projects related to lecture capture, hybrid learning, learning analytics and learning design. As a result of my leadership, these initiatives have now matured and are moving to business as usual, expanding the remit of my team and adding value to the staff and student experience. Beyond these initiatives, the real value of my leadership is how I have empowered, supported and developed others both within City and beyond. I really believe Julie deserves to win the Alt Leadership Award. She has a unique ability to create space for everyone in the team to grow, be themselves and be innovative. Whenever I come up with an idea for something I want to try, she always finds a way to make it happen. She's able to see things from everyone's perspective and that's what makes her a supportive and inclusive leader. Already an expert in her field, Julie brings some rare qualities in her leadership. She listens, she learns and she empowers. As a senior manager, she has given me the agency to become the kind of manager that I want it to be and the freedom to lead my team in the way that I want it. Finally, I am very active in the sector leading a range of activities such as the M25 Learning Technology Group and the recent USISA Technology Enhanced Learning Surveys. I am a senior CMOLT holder and assessor and align my leadership with the CMOLT principles and the framework for ethical learning technology by being actively inclusive and collaborative whilst taking a professional approach and acting with integrity. I am delighted to be recognized for this award. Thank you.