discussions and enlightenment emerging from this place and perhaps ideas to carry forward. Academia and industry, when you hear of this, they often seem as if they are opposites, antagonistic, fortunately in the field of design. Design is a practice and a profession, so many people in academia are from industry. Academia is a practice and industry, so there isn't that much to forget. Nevertheless, the goals of academia and industry tend to be a little different. Academia is about education, building a better human being, actualizing individuals. Industry is about getting the job done, often the beef that industry has about academia. We don't get enough students with the right training. Academia and it might say we're too much on our way. We need to develop these persons so that long-term we have benefits. But we don't get a wide range of perspectives here today. The way we're going to do this is each of the speakers here is going to speak for a couple of minutes about their perspective and then we're going to go have a second round. And perhaps as we go on with their time, then we'll throw this open to not long-term on these really thirst questions which our panelists here will see to address. Okay, does that work? Alright, let's get going. Go ahead, just talk to the other man. Very brief introduction and then we'll get into your set. Can you hear me? Okay, I'm Karshah from MIT Institute of Design. And I think we are a premier institute. And the industry, both global and local, is flooded with our students. That's what we like to believe at the moment. So yes, we're here to share and receive inputs and whatever shortcomings that you feel from your side would love to be able to bridge the gap. Hi, I'm Priyanka. I run a company called Factoring Design Studio. And we are rapidly growing. And the thing is that we keep interviewing freshers from college day in and day out. And so I have only two main points which I want to make here. One is that the UX industry is changing very rapidly. And right from the inception of the curriculum itself, if we can make a difference in a way that the industry is involved in shaping up that curriculum because the industry is changing so fast, the academics need to be updated with that. So that is one thing that, you know, it should be inculcated. And the merging between, you know, industry people going to juries and bringing in that industry perspective for the students. And as well as the professors coming to the industry and taking some bit of knowledge from there. So that is one. And the second thing I think when we interview people, what comes out very strongly is that design talking, the confidence is something which the college can play a very important role which currently we feel is a little slack. So that is the two main points which I want to mention. Hi, I'm Chandni. I am studying interaction design at IDC, IHA Bombay. And I'm also an architect. Just a few points, maybe from the postgraduate design education point of view, a few things that I'd like to highlight is that students come in at the postgraduate level from a variety of backgrounds. There are a lot of engineers. It's a good mixture. But so even though it's postgraduate level education, the starting level is quite, it's diverse, so everybody needs to catch up. So that's a challenge in terms of designing the curriculum itself. How do you design a curriculum that caters to graduate designers, graphic designers as well as engineers. And the duration of a course is typically two years and that's commonly felt that it's extremely short for someone to learn to design. And the one good thing about my school is that it's largely multi-disciplinary. Although there are distinct disciplines like interaction design and communication design animation, but we also work together. The courses are all overlapping and you can take electives. So the interdisciplinary aspect is quite a positive aspect. And about being industry ready, I think my point of view is that any job, any role has a certain loading period associated with it. So even if you're switching between jobs, depending on the job, you're going to have to take some time to learn. So it's difficult for a student to come out of an educational institute with being ready to start working on day one. And also education is not designed to create people who can take on roles. It's more about training designers who will be able to take on or adapt to any role that comes their way. Hi. Good morning everyone. My name is Shatrija Anand. I kind of finally made the shift towards academia after almost a decade in the industry. And what has been interesting in my journey of design education is spending time and trying to teach design to school kids, to college graduates, to college students, to working professionals. And that's a very interesting gamut of audience that you have because for the working professional, the understanding of design could be very different as compared to if you're in a design school or if you're in an engineering school or if you're in a school, like K-12 education and maybe even middle school and high school. So for the past three years or so, I think I've spent a lot of time in understanding these different aspects of design education, bringing in my vast experience of working in the industry, working in different service industry, product industry, working with a lot of startups. So kind of because we felt the need of a lot of design talent being required in different disciplines, in different projects, in different companies, the constant understanding that I had developed then was that we need to actually have more people studying design and as a result of which, of course, the shift to the academia part. And in particular, it's wonderful to see today when we're talking about industry academia fusion because we really need to understand both sides of the perspective, both sides of the coin in terms of what is required. And for me, the constant struggle that has been, and I think I would like to open that to everybody in the audience today, and I think a lot of my panelists will agree as well, this constant struggle to get quality teachers, and especially in design because as Murali was mentioning, it's about as a designer, you also have some amount of practice going on alongside. One strong request from my side is to have more people becoming teachers when you're in the industry, and I'm sure we can have more discussion on that as well. Thank you. Atul Manohar, I manage a very small team of UX designers at Informatica. Actually, in two minutes, let me quickly give you a very interesting story that I have built while I was working up. Imagine UX in India would have been a country, and the history of this country would look precisely like this. We primarily serve an industry called information technology industry, and IT industry in India is about 20 years old, little less or more. Industry started with that whole story of outsourcing, then giving services, services becoming more important, specialized services, and then developing products also. Now that we are era in wherein our guys are actually coming up with startups, our own ideas, our own products for our own products. Similarly, on the other side, on the professional side, we started with usability engineering wherein poking the problems in a product to maybe interaction design wherein I designed the whole set of interactions to now that we have started moving into user experience, design the whole experience, and now that we are conceptualizing the products. That's the way the professionals and the industry is kind of traversing in the history. Let's stand and see on the academia side. On the academia side, again, probably in the time of the usability era, academia was trying to figure out whether I should have some kind of a course to that, and they started coming up with some kind of a master's course. By the time industry moved on, the professionals moved on to the interaction design part. Slowly the academia started saying, okay, I have a two years master's degree course, and the professionals have moved to the slightly more strategic positions of designing things. Academia is also catching up saying that, no, I need bachelor's degree which needs four plus two years of design education. So now at this moment of time in history, this nation, there are four types of stakeholders. In the industry, there are business decision makers, and the professionals like all of us. In the academia side, there are teachers and the students. Am I making sense? So everybody is at a stage wherein industry is catching up. In the industry, entrepreneurs are starting up with new ideas for our own people, for our own country. Designers are sitting next to them, shoulder to shoulder. Industry is kind of trying to catch up with that movement. Probably is academia is also catching up over there, and there might be some bit of gaps in these two. So that's precisely what we might even look at discussing now. Hi, I'm Saleem Ahmed from CPDM, Center for Product Design and Manufacturing, IAC, Bangalore. I'm going to give a small bit of points on the history of your UX design. UX design as a field is in its infancy even now. It is perceived to be part of IT industry, and UX design is about 15 years old, you can say. Don Norman coined this word, user experience, in the late 90s, and ever since this has been catching on. This has its roots in ergonomics and human factors, which has been part of industrial design for the last about 60 years or so. So, though this being talked as UX design now, it has been into design for well over 60 years. The Ulm School of Design in Germany, which propagated this ergonomics in Henry Dreyfus, who made a great contribution into ergonomics and made that part of industrial design about 60 years ago. All these things now are expanding into this field UX design. There are about 350,000 engineers graduating annually in India, and McKinsey report says there are only about 25% of them are employable. So what happens? The industry trains them to become useful, and though they are not straight away being useful out of the college, and there is a big gap, industry academia gap is existing, and this will exist. My point is, it is still in its infancy, and it is getting matured. To get this fully matured may take couple of decades, and till then the gap will be existing, and there will be constant approach, market driven approach to bridge the gap. I am Sameer from UX Designs. Controversial, right? So we decided that we will have some spice. So I am going to talk about the good, the bad and the ugly. The good part is that there are a lot of schools coming up for design, and the design awareness in the country has been growing. Not just pressure from the students, but there is also this pressure from industry to have more and more designers, and so the awareness is growing, and therefore there are a lot of schools that are coming up. Also the good part is progressively what we are seeing in the last 8-10 years, that students are learning the techniques very well. Now they are well equipped to at least do information architecture, card sorting and wire frames and things like that. So that is the good part. The bad part, there is still a gap, and the gap is widening. The industry is running in different directions. The needs of entrepreneurs and startups are coming up very fast, and you are still doing wire frames. So that is the bad part. The ugly part is that industry is very well equipped to kind of come and contribute to academia, but they are not interested. I am sorry, but they are interested in doing business and not teaching students. Open to the forum. Great, great. I would like to directly address this. I can't speak for the other institutes, but at MIT we have a wonderful process where we do involve people from the industry and we have live projects as a part of class exercises where we invite people from the industry like yourself and we take on your projects. I think this is a perfect forum for me to invite startups and people who want, you know, looking for designers. We just probably give your project to a class of 50 students and you will get 50 design solutions. So that is answering your question directly. We are not asking you to teach them, but yes, please come and be a part of the exercise while it is going on, given your inputs. Like a typical, you know, it's like a real-world situation where you would be briefing your designer, you would be even be just briefing our students and it adds to their design learning, it adds to their portfolio and they are probably almost like, let's say, you know, one step already in the industry while they are learning and this is an ongoing process at MIT Institute of Design and I am surprised that there is this huge gap that is mentioned because, in fact, we are constantly inviting industry people. So yes, this is a platform for me to invite all of you on behalf of my institute, most welcome with your projects and solutions that you require for your new startups and clinics that you have in mind. Sabir, I would like to disagree with you that people don't have time to give to academics. In fact, we are on the panel of Past and Schools of Design for shaping up the curriculum and it really doesn't take that much time. So it just takes the, you know, the drive to go ahead and do it.  So if we can contribute to that and if we can take some time out, I think we should go ahead and do it. So yes, it is an ugly truth, but I think it's not very difficult to be achieved. I think it's difficult to, say, industry ready. So UX is a very specific, narrow sort of, I wouldn't call it a field, but even a profession. So design schools, I think, are very varied sort of skill sets involved in, you know, design and education. So when a student starts out, they probably, it's not a good idea, in fact, to sort of make a student choose exactly what sort of job they would want to work in at the end of their education. So it would be a little skeptical about the idea of industry influencing other curriculums. So you wait exactly the sort of designers who can do the work that industry requires that might not be good idea. So I probably was in the same situation as Sameer a few years ago. Like, how do you really get associated? I think many people in the audience here might have the same concern. You know, like, I have my startups to run, I have my businesses to run. I really cannot spend the time at institutes. The easy way out is, of course, to just, okay, as Ma'am on the left mentioned, you know, give a project to the institute and say, okay, my part of responsibility is giving the project to the institute and they will, of course, take care of it, get the projects done, et cetera, et cetera. Over the past three or four years, since I've worked with a lot of students, you end up understanding student psychology a lot more than sitting in the industry, right? One of the things that has been really interesting is students take feedback from industry people much more seriously than from academic people. The fact that there is this client relationship that starts getting evolved within the students itself, they end up taking those feedbacks, guidance much more seriously. So at the school that I, you know, like all the design in North Atlanta. Yeah, so there are different ways in which you can get involved. So one of course is giving projects, doing workshops, doing lectures, attending feedback sessions, et cetera, et cetera. So it's not just about just giving the project and being done with it. Let me open my book of history again and say, as I have tried to illustrate, the gap is inevitable and the gap is natural. There is nothing wrong in the gap at such. The only difference is now that we are looking at most of these academic institutes also as an industry, right? So it has to sustain itself. Primarily a couple of very nice examples from the data space that I work for are coming from Stanford University and maybe the MIT of U.S. where in research scholars, after completing their thesis, have come up with an innovative product in the data space. So I don't know why, but that is not so much happening in India. We have equal talent. We have equal industry interest and the teachers are also fabulous. So I don't know why MIT can produce some kind of a crazy start-up, very, very technical in nature, which is actually a produce of a PhD thesis. And I don't know. I don't know any more examples from Indian universities likewise. I would like to disagree with Chandni. She said that U.S. is narrow. But I look at it as a very wide thing. You know, I look at it as a holistic thing. Because U.S. is addressing the physical aspects, the mental aspects, the emotional aspects. It is giving a holistic approach to design. And that means it is covering a big spectrum, wide spectrum of areas in its fold. So, you know, this as a discipline will take a long time to mature. I expect about two to three decades for it to come to some state where the demand supply gets too close to equilibrium. Till then, the gap is expected to be there. And that is a challenge. And there should be constant interaction between industry and academia. People from the industry come and be part of the academia as part-time faculty or give projects to the institutes. And part-time employees in industry or giving consultancy will kind of narrow the gap. So, I'll just make one quick point about the gap. What we are seeing is the gap is not so much of the techniques and tools that we used to see earlier. But it's more of the softer skills and design management now, which is getting more and more important. And as, you know, design leaders, we all know how important it is to actually stand in front of 50 stakeholders and sell the design, rather than actually doing the design. And is that in the curriculum? I don't think so. And I would again like to make a point that, you know, I've studied in the US and I'm sure there are many people who have studied outside, right? There is a very strong research base in all those institutions, which is directly applied research, and the research actually is being used by the industry. Where is that research from academia? That we can use. I would want to use something that is very useful as a new technique or tool or research in my work. I would like to give something more, but is it coming from there? No. So are we equipped? We are not because we are busy making money. Okay. What I'm going to do now is throw this open. Throw this open to the audience and we have... Let me... We have a whole bunch of hands. That's the first one I saw. So you're going to be the first. And then if you want it to be addressed by a specific panelist, say so. Otherwise, you know, leave it open to anyone. And keep it brief. Not a comment, but a very brief question. I have... Yeah, I'm happy to hear from anybody who has a comment on this. What's your name? Which institution? If that's okay. And then what's the question? My name is Adil Lakshmi. I'm not a student. I'm a working professional. And I studied outside of India. I have a question. We've been hearing a lot about the gap in the industry in academia. Have you considered anything like a professional masters program or something of that sort? Where, similar to what you were saying, I think about people from industry coming and giving you projects. I would actually say instead of that just being a class project, a professional masters is something a company would tie up with an institute for a longer period of time, maybe a few months. A working project that actually delivers results. That's the core of the professional masters. That's how students learn what techniques or the theory in terms of applying to a real project. Thank you. So has academia in India considered having this professional technical kind of projects where they actually work on real problems for industry? That's a very nice point that you brought up and we'll definitely take it with you outside. Yes, we do have we're touching that to some extent when we have internship programs and our students intern for about a month or a period of two months and that project is also mentored both by the faculty and the external mentor, the industry mentor. The student gets a complete different view when you get the real experience and they come back to us for all the research and just kind of a checkpoint as to everything. Yes, but I like this idea of having a six month incubation period with a company that would be a perfect kind of an engagement period. And what he was talking about yes, I guess when we have these kind of long projects probably we need to just put them up on a platform and share with the rest of the world. So yes, we could have more people and more companies coming up to us. That was really nice. Thank you. Anybody else wants to address that? Are you okay with that? Okay. Next question. Yes, go ahead. Hi, I'm Dabanchu from National Institute of Design. I'm Dabach. So we always have heard of this thing like abstract and concrete. So when we are going into the abstract field we let lose our horses and explore and then come to something concrete cutting down on things. So I feel like academia that we are talking about lets us to explore and industry tries to bring down to so I think there should the question is that I feel that this bridge should be there and why to like why to bring both together? So let us use a natural gap and that's the way these two things are. Why should there not be a gap? What's wrong? Okay, go ahead. Anybody wants to why do we have a problem with the gap? Actually, I completely agree with you. The gap is completely natural. It is there to stay is the way you look at the gap. For example, a couple of internship projects we have done and from the industry side always they will crib that if at all there is an intern working with my team, there is a lot of babysitting I invest a lot and equally the student side would also complain saying that there are just too many constraints to work for and it's very hard for me to fit in my free flow thinking into a very structured process. But I think the beauty of this the gap is the student has to swim that gap anyways on his own. Earlier it was a free flowing just choose what he wants to design whereas in an industry he'll have to address one particular problem and the beauty of the whole process of learning is having that gap itself. So no pain, no gain. So they learn through suffering, salvation through suffering. So how easy, how difficult or easy is it for both the sides to jump into that pain. Industry does go one step further saying that hey, I don't mind paying this guy these three interns they are stipend for six months I don't expect anything out of that. Fairly enough. So similarly I mean it should be both side pain it should not be a one side pain for sure. Anyone else? I would like to make a point. I think there is this perception that industry tries to curtail things and it is a very concrete stuff which we are trying to make but in all cases it is not true. Yes I mean I completely agree to your point that in the college you should be allowed to explore you should that is how the thinking opens it should not be curtailed at that level but there is in the industry also when you come you have projects in which you explore you are allowed to do the you know whatever you want to do to create a difference in the community of the society. So it's not a curtailing so that perception of curtailing by the industry I think is kind of wrong and just bringing the perspective of the industry into whatever projects you are doing might make a difference. So are you saying that perhaps there is this erroneous perception that exists and if it exists perhaps industry has not done enough to erase this perception they should or should not. What do you think? Anybody? I would like to address that you know there is a difference between dreams and reality and you know it's good that you know you learn blue sky thinking and the project goes on and on reality doesn't talk like that there are deliverables there's time commitments and there's money and there's effort and that's the reality of the industry today tomorrow yesterday as well I'm saying that you know while you teach blue sky thinking which is good to expand your horizons why not also teach that there is something called time and effort. There is a timeline to everything and there's cost to everything that is not being taught. This gentleman yes go ahead is there should we give him one? My name is Sudheer Sharma I'm a designer I sometimes teach as well and I write a lot okay I have first some comments to make. No quick questions Sudheer we don't have enough time. No I'm going to make a comment because and you have to listen to it no I don't think we should. Can I just make a perspective Sudheer has a pool magazine which he runs yeah okay thank you no I don't think we should misuse the stage anyway so my perspective is that we are treating the design education like it used to be producing engineers earlier. Preparing people for manufacturing industry so we need to prepare people who can just go and thank you get on to assembly lines and start producing you know bikes and motorcycles and cars design and creative industries work in a different way and I think yeah the way we need to look at that is there is a gap between academia and industry and I feel the gap has to be on the reverse side academia has to lead the industry not being led by the industry there's a problem in Indian situation here if you see design we are celebrating 60th year of design in India this year or last year design has been in India before people know what design is few institutions have been around UX has come into India 2 years back 3 years back I don't know you guys would know and it's being led by the industry so the academia like many of the colleges you are representing are just trying to fill in the gaps so that more and more people can be employed by the industry and I think I completely agree with what Sameer is saying we are producing people giving them skills and tools so that they can go and get jobs okay that's not the role of the academia academia needs to lead the industry if you actually see where all this is coming from people who have been educated outside the research part I think that is very valid and touched upon the research part is what is leading the industry I feel that part is completely missing from our education I have a big problem with okay 30 seconds I haven't finished it I have a big problem with we are confusing education with training that's something which we should not training is fair you know two years course train people and get them jobs that's a big responsibility we have in a country with so many people we should do that but I feel the academia has to lead the industry and I find a problem there the kind of faculty which is joining the design education I believe and I can guarantee that none of you are actually UX trained people are trained into UX because of your experience right we we're trying to now make people who are UX trained and what would they do they would do UX when they go out and would UX be needed in two years time we don't know anybody wants to address this the key issue being academia should lead industry is it possible for academia to lead industry if industry is at the leading edge is there a way in which industry can participate with academia in helping academia lead or is there a different perspective that you have I have just one thing to that and I completely agree with Sudeep's point that it has to lead but the fact of the matter is that if you go abroad and study you will see and find that you know there is this difference in the curriculum in the way teaching is being carried out in institutes in India there is a huge difference and that is why you see people who have studied abroad and when they come back they are instantly you resonate with them they bring something new to the table so that is what leading by academia I think is what you are trying to say and that I think in India we as people who are employing and who have people joining us have not found from students coming from India I think I completely agree to that point anybody else? just to quickly looking at the academia side as well I think it is also the student industry in India is very driven by this world called placements and that is causing a lot of problems because you want the academia to maintain that gap and that abstractness but at the same time the students are also concerned about placements and they really want those placements the parents when they start the interviews for applications for the schools first thing they ask is what is the placement of your colleges rather than asking whether you are making them a good designer or not so that is one of the bigger factors that I think we also need to have a separate panel discussion probably later on is that a reality you are willing to accept and work around it yeah I think so there are different working around it thankfully entrepreneurship has given this huge boost to that the notion that it is not just about the placements and you can actually go ahead and be your unique designer in what was your own capacity and then start your own stuff and you really drive with what your own vision of design is but again it is another counseling for the parent session because they don't understand this notion of entrepreneurship in the first place at all so again it is a more complex, wicked problem as we say in design it is not just very simple as that sounds Atul, Salim, Sameer any thoughts on that interestingly that gap we are talking about is not a river full of piranhas always there is another beautiful island over there called research and frankly in my mind whether industry leads or academia leads it doesn't really matter so that research island is a beautiful island wherein industry could park some of their guys over there give them the freedom they don't get in the industry and academia could park some of their researchers and say hey you are free from your regular stuff and you could look at the business and try and explore it so I think that's the beautiful sweet spot both of the parties could kind of jump into Salim, I agree with Atul he said that point very nicely it is not important who leads whether the industry or academia finally should address the issues to the society for example in CPDM we are currently offering courses at the master's level in product design and many of our students are getting into in a UX design interaction design and that is making us feel we need to do that more in the education level itself and we are probably from next year going to offer courses in UX design maybe when we go to expand this program from product design we are likely to introduce UX design as one of the programs that may happen in the very near future so depending on the demand from the industry in academia we are offering programs to cater to the need Samir you want to say something? I just want to make one point it was about the employability any designers who don't have a job today there is no problem absolutely the demand is very high the supply is very low and the ugly part of industry I am coming on your side the ugly part of industry is the managers who are hiring these designers don't know what to look for themselves you addressed a couple of concerns and I am so sorry but I really have to address this concerns the biggest thing I would like to the most important thing I would like to point out is the fact that right now I see the industry is led teams having led design team heads are primarily people who are from engineering backgrounds or from MBA backgrounds and that is what really really strikes us as like how is that happening we need more designers to be taking more lead roles and at MIT speaking from my institute we are creating those leaders it is our endeavour to create those leaders and the next five years all our students are going out there we are hoping that they will be leading this design teams and that itself would then you can say that the ball is going to be in the other court and is it industry leading or academia leading these students who are professionals who are leading so how is it any different quick we have a few minutes left so quick questions quick responses and we will try to get as many people as possible go ahead my name is Nikesh I am the founder of a start up called events high I come from academic background PhD background but now an industry in a start up so essentially two questions one is how can we share data we have hundreds of user interviews can we share that with the students what is your second question the second one is do you see there is a different need for start ups versus large corporates in terms of education two questions how does industry share data with academia second is there is a difference in needs between start ups and large corporations and how do you address those very quickly we do have a proper process as to how these projects are handled and depending on how you showcase your project or a brief to us we assign a team which is the combination of mentors faculty members and designers and yeah that is how you can exchange your data it is very if you want a nontal disclosure everything that all is taken care of so you don't really have to worry about that second question large versus small how do you address the difference the thing is that we promote our students we have big companies coming to us and pick up our students right away right from college itself when we have a placement program happening but we always tell our students look at these smaller companies look at these start ups because that is really some good work yeah but how do you treat them differently looking at one thing how do you address the needs differently is there a different approach I think we give them the same platform as we would give any big company it is not like we differentiate let's try any other view anybody else comments on that from the data security side I think the smaller companies are at an advantage because they are not bound by all these stupid legalities so if I want to present one of my case studies like this one I'll have to take a couple of approvals from my legal as well simply because in the US there have been number of cases wherein because I showcase my products in a particular way there was a huge legal case and my company paid a couple of million dollars so actually the smaller companies are at a much leisure they don't really care about all this stuff and frankly in my mind the more you share your new ideas the more they will grow so I don't think there should be a reason for you guys to be secretive about your ideas okay let's take another question there were lots of hands over there oh she's got the mind so I have a question until a decade back I saw everybody wanted to be an engineer or a doctor now everybody wants to be a UX designer it's a bubble which is rapidly growing and it is going to burst someday so if the industry has really realized the value of design why don't I see that value in product design or why don't I see product design or furniture design or any other discipline of design growing as much as user experience design and that to only in mobile apps or web platforms if companies are really realizing UX design let's try to address that so one is the claim that UX is a bubble which some of you might take have different opinion the second is if there's so much design is such a big thing how come we don't see evidence around us right? one more concern here is very quickly I'll say everybody wants to be a UX designer an engineer today wants to be a UX designer a developer wants to be a UX designer I don't see anything bad but how do you realize that who actually has the passion for user experience and wants to become a UX designer so how do you sift them in terms of select so one according to me UX is not a bubble that will burst it will evolve it is user experience now it will evolve into something the other question is see we are now talking yesterday into experiential zone brands themselves have become experiential and that is why UX as a term has grown so fast and start-ups have come in and there are a lot of engineers and these are all technology led start-ups technology people they understand the value of UX and that is why they are coming into right now and it is according to me and it will evolve into something else maybe a couple of years later but it is the matter of keeping yourself updated and growing with it anyone else who likes speaks to this I think it is just a natural people are just going with the money it is as simple as that UX companies pay well all students irrespective of what you study product design, fashion design design management doesn't matter I can pick up the skills by taking two courses I will get into the UX discipline I will become a UX designer the transformation is over anybody else actually on the bubble side I completely agree there was an internet bubble product design bubble also little small one in India but that was also there a couple of years back so you are completely right on the bubble side but that is life life is kind of cyclical so we will have to run with that for sure the screening I believe it is very easy to talk about design just give them a design problem and you figure out whether I need this candidate or not this field will keep expanding until it is filled it will take time and it is not a bubble until the gaps are filled it will keep expanding and UX is a big field where it is not only involving designers engineers can contribute to UX as engineers researchers can contribute as researchers and so on and so forth so it is a big spectrum where people of different kinds are required to contribute in this field not only as designers and perhaps if there is any consolation in that your design training will not go waste no matter which field you eventually get into perhaps just one quick question really sorry go ahead we will have to that corner of the room hasn't been addressed have you got the microphone until he gets the microphone go ahead and ask I am a designer working for an I.D. company my question right now it is my observation that people are concerned I am a designer from my academy I am a designer from the industry I am a non-teketurn designer my concern is do we really concern about what our qualification is or are we concerned with people who are sensitive to design people who are conscious about design and people who can really make an exception so it doesn't matter what your training is if you have the right attitude right skills and so on and so forth not at all in fact our company has people who are photographers and now they are doing UIUX so it is the design sensitivity as you said if a person is sensitive to design he has a place in the industry nothing can stop them one last question here last question it is faster than getting the microphone run run run run ask the question my name is Leela I am allowed teaching actually so at the academy I am really using any UIUX techniques in recruiting the teachers professors thank you please last question I want to ask a specific question industry and academia was that a comment or was that a question do you use any methods do you eat your own dog food so to speak well I mean I can't speak looking at we have like our seven disciplines disciplines and there is a vast experience 30 seconds how do we choose faculty is what you are saying brand really we look for people who are in the industry and who spent a considerable amount of time doing a certain kind of work and wish that they can lend the experience to people to the students and the other part is we just kind of lost that one what was it how do you recruit and I think you have addressed it let's go to the somebody who was we recruit people with real experience we recruit people with real experience hello sorry come come come I have a specific question very specific are our industry and academia producing research paper which are specific to interaction design cognitive psychology if no why so basically this is probably related to what Sudhir asked to so have we invested academia invested are we mature enough invested in research to produce original research we are in the process to be very honest we have just launched you know UG program for UX and we have just our first batch of PG UX designers will be passing out in December so you are most welcome it will be all out there very good so there is something coming out of the pipe already 30 seconds addition to that so it is very popular in the IITs they have that whole culture of research and professors and students working on projects participating in different conferences it is very prominent and I would love it when all the other industries also follow suit industry now all the research is done in ergonomics, human factors user research they are all part of it so so industry folks here we find in the west very frequently industry like for instance companies like Hewlett-Prakat investing in Stanford is there any move from industries here who have deep pockets to go in and say here is let's set up an XYZ company center for design is there any such actually that pockets are not deeper anymore if a company is tracking their own results on a quarter basis it's very very hard to invest but most of the companies I believe are investing a little though not enough for sure next question alright so do you believe that design is dynamic this to the academia directly is design dynamic what do you mean by that is it ever changing it's an organic process to begin so other academia teachers that we have going out is it being updated with what is ever changing yes we are constantly engaged most of the staff is engaged we have projects you know ongoing projects in the industry and sometimes we are involved with projects in the industry with the students so it's we are very much hands on I can speak from my institute we have many of my faculty members so good question I would like to again repeat my offer of last years that many teachers should come and do certain projects with us we would love that we would go and teach some question are you waiting for academia to come and ask you are you reaching out to them and sending out invitations we are already doing that we are collaborating with a couple of design schools already defining their curriculum so impact at that level also kind of training the teachers as well in terms of UX because there is probably a little understanding of that we are reaching out already one question a lot of design schools have emerged outside of our traditional bureaucratic hack need university system which is way behind times is there any way in which you are influencing our traditional universities I am keeping leaving Indian Institute of Science out of this it is not a traditional university so I will narrate a story in 2003 I went to University of Pune and there was a very eminent vice chancellor at that time who is also an economist who was also on the Indian panel of economists and we proposed to them that we will start a program of human factors engineering we will take the responsibility and collaborate with outside universities with known people and we will do everything for them so the idea was welcomed but as soon as we went out we had to stop alright the old story ok go ahead one question who is that yes go ahead one industrial problem here when we do job job description we always see UI UX skill set in one and how do we address that is this a right forum for that do you want to speak to that absolutely I would like to address a little bit of that UI UX doesn't go hand in hand UI is just an output of what we do 80% of UX again to my point of the ugliness of the industry the people who are writing these job descriptions don't understand and they want interaction designer, visual designer html everyone in the same I don't understand ok maybe folks who are designers should spend time with these ignorant folks out there educating them maybe ok we have time for just one question sorry yeah I'm right here no you had your chance hi sorry I wanted to pick up sorry ok sorry go ahead while we were talking about designing being dynamic so do we have this notion that education stops at graduation or do you think considering how everything is evolving and changing so quickly do we need continuous training as you're on the job does industry support or value training while on the job talks why must it stop you're absolutely right most of the industries do have very specific budgets for upgrading my own skills and it's an yearly budget almost every industries do have that definitely ok well we've run out of time folks I'm sorry we would like to extend this for another half but these folks are around please accost them have discussions with them right I hope I'm not jumping the gun there thank you very much


community matters here. Aloha and welcome to Stand the Energy Man on Think Tech, Hawaii. I'm Rachel James, also known as Rachel the Energy Woman, filling in for Stan Osserman. Today we are excited to be delving deeply into the intricacies of energy through a conversation about how communities, governments, developers, and others are thinking about harnessing power. I'm pleased to be able to spend the next half hour on this discussion with our friendly third Friday officiant on all things energy and engineering, Mr. Ryan Wibbins. Welcome Ryan. Hi, thank you. It's like deja vu, like we've been here before. It feels like we've been here before. Thanks for joining us on the show today. If you could, share a little bit about yourself, what you do, why you do it. Okay, sure. So I come on every third Friday and since I'm an electrical engineer, Stan brings me on. We talk a little bit about energy. He's the energy man. Yes. I come in and I talk more about microgrids if possible, energy usage in the utility grid in general and really wherever he wants to go. It's tiny homes. We've talked tiny homes. Tiny homes and toilets. You bet. Okay, so we're going to start a little energy journey today about community solar. And so in the context of microgrids, or perhaps in another context if you wish, what is community solar? Sure. Community solar can and can, could not be associated with microgrids if we want. Let's keep it, let's say it is associated with a microgrid first. Sometimes this is a little bit easier because when you think of a microgrid, you're going to think geographic. Let's say there's a new little community being built somewhere or an existing one and they together want to build their own solar array. Maybe they can't put it on their houses because of trees, but they've got a little land that they, that they cooperatively own, they put in a solar array and they use that to help their local community as well as in agreements with their public utility. Okay. If we want to call it a microgrid, maybe they also have the ability if the local utility goes down for whatever reason unplanned or planned, they can stay powered off of their solar. That would be like a community solar microgrid. It would need a lot more components than just solar to work, but that would be a microgrid. In other ways, community solar is addressed, is really a community localized or not localized geographically. Maybe it's every other house, at least people want to support an energy project and that being community solar. So it would be like people in the community being able to support and receive benefits from that project. Okay. That second one make a little sense because that second one can get a little trickier. It sounds a little hairy. It's like, what do you mean? I'm part of a community, but I'm not part of the community, but I'm paying, but I'm not benefiting directly. We could, we could tease that out if you'd like. Sure. I think that's, that's one of the interstices of the, of the current docket actually with the community solar for Hawaii. When you install a solar panel on your house, you're receiving the benefits immediately. It is going to you to your refrigerator during the day. And then if there's anything extra, it's going back out to the utility. So when you buy that solar panel, you are the direct recipient of that power immediately. It's physically on how it's placed. It's going to go through your house first. When you talk of community solar, maybe it's a renter that is renting a house, but they don't have the ability to put solar on top of that, that roof, the roof that they don't own, or maybe you live in a high-rise and you definitely don't have that ability. But you still want to have an option to support a renewable energy project. Now you could go into a community solar down the road, maybe on the other side of the island even, and help support that project. Now as they produce power, it's supposed to. There's a lot harder way to make this work, but it's supposed to offset your bill as it's producing as you're consuming. Now there's a lot of, I want to say no-no, it doesn't technologically equate that way. Just because you produce it over there, it doesn't mean it goes through the cable and it just shows up at your house. So there's a very high technical difficulty and that's why traditionally that has not been allowed. Interesting. So you mentioned a docket currently. We can delve into the docket briefly. It's a deep and an expensive one. That's so Hawaii is foremost, as I understand, in establishing a community-based renewable energy program. So one that builds on the concept of community solar but allows other renewable energy technologies to be kind of implemented in this community solar style. And that started back in 2015. We had ACT 100 that essentially required our utilities to put forth a tariff for community-based renewable energy. So from 2015 to now, the docket's been progressing in the Public Utilities Commission and they've been discussing what a program framework would look like, discussing what a tariff would look like. And so we've gotten pretty far along in that process, but we're still awaiting a final order. So as we think about community-based renewable energy and we talk about communities and community members coming together, it's an interesting discussion to kind of identify who those community members are and what that looks like for people engaging. So I'm not sure how familiar you are with the docket, but as I understand it, there's subscriber organizations, which are kind of the convening entities that would gather subscribers or community members, and that subscriber organization would develop the project and essentially sell that power to the utility. So as a community member, if I want to invest in a project, what are some of the things that I might need to be aware of? As a community member investing, I think, I don't know, actually. I think those entities need to maybe be a little more developed before you can start asking the questions directly to them. I would start asking questions, well, on what the rate is and how it's going to affect me, comparing it to your current utility bill, maybe another part of those questions, but it'll get much deeper depending on how those specific entities want to operate because they're the ones going to be maintaining and operating the unit. If that were to go down for maintenance or need a major repair, how does that affect the subscriber's short-term and long-term? Okay. So for engagement for an entity like the one in which you are employed, I guess how does an entity like that engage in a community solar project? Sure. So I work for Burns McDonald. We are a consulting engineering firm with construction services as well. We would get engaged in a project like community solar when a developer or a client already has maybe a project that they want to develop. If you wanted to develop a community sort, you would come to us and say, I'm ready. I think I have some land here. I think I know what I need solar-wise. I think I know what I need storage-wise because that's part of that docket now too. How can you help me? And then we would come in as the engineers and really draw that up for you and how that would work out. I see. So we're going to role-play a minute. You can be yourself. I'll be a developer. All right. I can do that. Okay. So I want to build a one-megawatt solar and wind farm somewhere in Hawaii. What information do you need for me to start that? Do you have somebody that you want to buy from exclusively? Buy from like a manufacturer. If you're going out and buying a specific manufacturer that's going to really put me into a down-the-road sooner because I don't have to think a lot. I have a selection of let's say part numbers to go from. I want to want to know where you want to put it and then that's really where we'll start and then we'll continue with some conversations. Okay. Beyond that. Do I need to know things like wind regimes and solar penetration and do I need to know as a developer the details of the area that I'm thinking of or do I just need to pick a spot on the map and say, that looks open. I'd like to build there. If you already have it, I would recommend even coming to us and help doing that study to find out how much sun and wind you have available. Okay. That's just us. Any, not any, but there are plenty of engineering firms or consultants out there that can help you with that. Okay. But yeah, geographically you're going to change a lot as far as your solar and wind capabilities. Okay. So if I come to you with a plan that I think is pretty well-baked and then we identify that maybe that's not the best place to place my resources, how much direction do you provide and what might be a better alternative or do you just wait for me to do a little bit more due diligence and come back with a better plan? I'm sure if you did it, it's probably perfect for the first time. Yeah, probably. But absolutely we would and many others would provide recommendations on maybe a better option for you if available. Okay. So here in Utopia of community solar being ubiquitous, what is the community solar project in Hawaii look like? Or community, we'll just say community renewables. Sure. Community renewables. To me in my own Utopia, it's very, because I'm going to come from the electrical engineering side where I kind of wanted to be, I'm going to say easy, but let's say simple. Yeah. Let's say simple. Simple is good. I would want it to be geographical. I would want a community such as if it's Kanaeoe, that community going in and supporting their renewables in the Kanaeoe area. Okay. That has a lot of benefits too politically where when you start putting in a wind turbine or a solar panel somewhere, the people that are supporting that are literally supporting it financially too. So they're not also supporting it financially and then raising their hand and say, well, I don't like the way that looks. Right. That seems to be somewhat common. So politically it has that nice part, but technologically it's much easier to prove and use the power consumed versus where the power is generated. In other people's utopias, it's anybody can support any project going on anywhere and they're not necessarily responsible for how an electron gets from A to B. That poses a lot more technological difficulties. Okay. And politically it's kind of attached too because somebody is supporting those power lines. If you're producing power, on a small island it's not as big of a deal, but if you're producing power really far away but you're consuming it in a completely different location, it costs money to get power to you where you're located and that might not be associated generally with the same cost for the power that it costs you to make it somewhere else. So it's not really a one for one, even though we'd like it to be. Just science. I didn't invent science. You just manipulate it or use it right? Use it. Use it. Yeah. Move it like in a good way. Use it. Yes. Everything in a good way. So you mentioned something interesting, localized use and production as opposed to sending power and taking power to move power. You also mentioned some political things which always get a little bit exciting. So in terms of your average Joe who learns about a developer who's coming into their community to propose a project, do I as average Joe or Jane? Do I need to know anything about renewable energy or do I just need to come to a community meeting or talk to Burns and McDonnell representatives like how do I engage to be an informed participant? An informed participant in just consuming or producing renewable energy? Commenting generally on a community renewable project. Sure. I think starting in the community is a great place to start. Any time that the local utility is putting on events, they do a lot of events that are supporting the renewable goals of 2045 and how they're going to get there. Those are probably the most impactful because that utility will have the most impact on all consumers. Yeah, actually that's going to be your best place to go. Is any event that the local utility will host? Okay. So average consumers should engage more readily with our local utility. Absolutely. Okay. And Hawaiian Electric is very open. They are wanting that community involvement. They are very aware that they're not trying to get too far without the community. As utilities on the mainland have learned that when they're not engaging their customer base, not everybody is as happy and HIKO is taking a different approach for that. I see. So in Hawaii generally, I understand that we take different approaches and HIKO taking a different approach in many ways just being the only state to commit to 100% renewable energy by any identified date, but then taking on the challenge of distributed energy and really what that looks like for a multi-island state. So as we think about geographically how the different counties have very unique characteristics both as individuals and population but also land and wind and rain and sun, what does community energy perhaps look like in a place that doesn't have a lot of renewable assets immediately available? So perhaps I live in a place that doesn't have a great deal of solar or wind. Is there still an opportunity for me to have localized power? Renewable localized power? There is. It's just more difficult. The leading renewable energy generation sources are solar and wind. They've been around a long time. They're improving in their technology and getting cheaper in cost. Those are going to be the easiest ones. When those come off, the availability list, hydro has already been done wherever it's going to be done at this point, so let's scratch that one off as well. Now you're talking about transporting your renewable energy. So you would need to be connected to a grid where renewables are being produced on the other side of that grid reporting. Now you need to be starting to transport a renewable resource. That could be something incredibly effective with just a battery.  That in theory would work. Or maybe down the road there's some type of a way to actually transport it like a renewable energy that Stan loves and you guys love is hydrogen. I'm definitely leading you to hydrogen. Hydrogen is a great energy density resource that can be transported. So we will delve deeply into hydrogen. Thanks for letting me lead you there. Yeah. But we'll take a break for a moment and be back with our audience in just a moment. Guys, don't forget to check me out right here at the Prince of Investing. I'm your host, Prince Dykes, each and every Tuesdays at 11 a.m. Hawaii time. I'm going to be right here. Stop by here from some of the best investment minds across the globe. And real estate, finances, stocks, hedge funds, managers, all that great stuff. Thank you. She's sad. All the better to see you with my dear. Research says reading from birth accelerates the baby's brain development. And you're doing that now? Oh, yeah. Yeah. This is the starting line. Push! You're dead. Read aloud 15 minutes. Every child. Every parent. Every day. Aloha and welcome back to Stan the Energy Man. I'm Rachel the Energy Woman and in a moment we'll be talking with Ryan Rebens. Wubbins. Gosh, R-dubs. I'm going to go with R-dubs. That works. Okay, great. Thanks for leading us and allowing me to lead you into the space of hydrogen. And so we last spoke about energy density, hydrogen being a good energy carrier. And then we chatted a little over the break about just the space required for renewable energy. So as we talk about community-based renewables, we talk about solar and wind and just generally how we access programs like this, what does space requirements have to do with that discussion?


Alright guys, so this is my guest Toby. I'm going to be doing a cut on him today. You may remember him from a video about a year ago. We did his going off to college haircut. Well, this is now his going off to college for the second year haircut. So what we're going to do is a nice clean tapered look. We're going to fade in the back hairline a little bit, something I like doing on him now. And I'm also going to work with you guys on working around the head shape. We're going to start off with our Andis Supra ZR Clipper. This is a motorized clipper that also uses the metal blade. So we're going to use a two blade, which is different than a two guard. Two guard is definitely longer. So you can see how nice and clean tight that two blade goes. So I'm going to work my way around his hairline. Now I want you guys to notice that kind of U shape that I'm creating with the clipper. So all my clipper works going to work on that U shape. It's going to taper diagonal forward towards the front. I really want to follow the contour of his head shape. This is something that I've actually learned a little bit about as I've been doing men's cutting videos and just men's cutting in general, watching other people doing it, watching barbers do it. I really like bringing that hairline a little bit more forward, really focusing on that hairline. So as I learn, I want to share with you guys things that I learned as well. So working my way through, just following like I said, the head shape around and dipping it down just a little bit right into the front. The other thing that I really focus on now that people have brought to my attention throughout my men's cutting and something that I've been guided and learned from is really focusing on the balance in the very front. So making sure that that weight in the front is balanced on both sides of the head. So when you look at them straight on in the mirror, you can see that there's balance within the haircut. So obviously, I got a little bit of a weight line. Now I'm going to work in with my three and a half blade. Now Toby's hair is a little bit thicker, so he will still have a shadow shadowed effect on that line. We're going to go in and do scissor over comb later. So I'm not a barber. I'm a hairdresser and I really like combining techniques, scissor over comb, clippering to get my end result. So notice my comb now is diagonal forward. I'm not working diagonal back because that would take that weight line away. I want to keep that weight line moving towards the front of his head. And then as I work towards the back of the head, my comb will go more diagonal back. So again, following that head shape, keeping consistent with my technique. That's the biggest thing in hair cutting in general. It doesn't matter if it's men's cutting or female. You want to make sure that you're consistent with every single thing that you do. It will show off in the end result of the cut. So working scissor over comb tips here, just making sure I'm keeping that comb nice and flat to the to the head. And then I taper it out as I get towards the end. I also had that steady blade staying and I'm just moving my thumb to create the actual cut. So that steady blade stays there. And then I come down and cut. This is a scissor that we have for sale on freestyleneducation.com. We sell all Mizetani scissors. This is a seven inch blacksmith fit solid scissor. This is what I do. All my scissor over comb work with. I got a customized titanium finish on it. That does add a little bit extra to the cost. So just so you know that going into it. But you can get a regular metal finish. Works just as well. For me, it does add a little strength to the blade. But most of the time it's just for aesthetics. I like the black look of the blade. So now I went through. I did all my scissor over comb work. Now I'm going to work through the top. I love having this new studio and being able to show that top angle. Because especially in men's cutting, just being able to show off exactly how I'm parting the hair. So I section everything. I'm not clipping it away. You'll see some people clip men's hair. I try not to if I don't have to. So I'm going through just really making nice clean partings and parting it away. I switched up my scissor. Now I'm using the Mizetani Type-K. This is a five and a half inch. I don't like to use a seven inch scissor in everything I do. I use it for stretch and reach when I'm doing scissor over comb work. But now that I'm working nice and tight, I want nice clean lines. A tighter scissor makes it easier to work close to the head. It's also a stronger blade so you get stronger lines. The smaller your blade, I've talked about this in a lot of videos. But if you're new to the channel, the smaller the blade, the stronger it is, the less it pushes the hair. So when you look at people that they always say, Sassoon people use like a four inch scissor, four and a half inch scissor. The reason they do that is because there's no reason to cut the entire length of the hair at six inches or whatever. And also your blade gets weaker so it pushes your lines. If you want clean lines, you want to work with a shorter scissor. I find for me personally five and a half inch is the most comfortable scissor for me. And I just make sure I take nice small sections and I get nice clean lines that way. So technique wise here, what I'm doing is just bringing everything vertical across the head shape. We could call it horizontal. Depends on how you learned it, what school you went to. But just straight across the top of the head, towards the front fringe area, I'm lifting everything straight up. So what I'm getting by doing this is a square feel on the top and then it's pushing that weight off to the parietal ridge area.  What that's going to do is I'll go in a little bit later and detail that but I'm keeping the weight just on the sides. You'll start to see it right here as I'm combing it up. You could see the how it builds up at the parietal ridge. I like that look. I am going to go in and clean it a little bit. But first I'm going to do, I'm going to part his hair where he's going to wear it and then I'm going to work on the front as well. So you can see I lifted the hair up. I elevated everything cut it straight across. Like I was saying, it pushed the weight to that parietal ridge area. So now what I'm going to do is just use the loose teeth of my comb. Go through working scissor over comb just to clean it up and match up the top and the sides. That's how Toby's going to wear his hair. Next thing we're going to do is we're going to take a 1A blade and I'm going to take the hairline a little bit tighter. What that's going to do is almost make it not invisible because I don't want to go skin tight. I just wanted to bring it a little closer so that I don't have those really harsh squared off lines. This is something I've learned over the years, not only from just personal preference, but also getting critiqued through YouTube. So thank you to those people out there. But just taking the hairline a little bit tighter and then going through scissor over comb using the fine teeth of my comb to get those hairs nice and tight and then going through and getting rid of that weight line. So the last thing I'm going to do is work on the fringe area and this is kind of a cool little trick. What I do is I comb everything down but just a slight bit about a half inch of the hair and I lift it into my comb.  So just cutting that guideline and then I take another parting across about another half inch. I've overdirect everything forward. That's going to push some more weight towards the back but it gives me those nice short fringe pieces that are easy to style. So instead of giving him a long fringe, I like giving him a little bit shorter fringe, a little more length towards just above the fringe area and then a little shorter into the crown. So to me it's just transferring that weight a little bit, a little bit shorter in the front, pop that front up and then have your weight sitting just above that. So we're going to be using Mitch Double-Hitter for our shampoo and conditions. So the cool thing about this product is it's a 2-in-1 so it's great for guys that aren't looking for using a shampoo and a conditioner. So it gets the hair clarified but then it also goes through and conditions. It's also a great body wash. The other product within Mitch that's a shampoo is Heavy Hitter which if you use a really thick kind of paw made or something that sticks to your hair that you really need to clarify that's a great product for even a once a week shampoo just to get all that product cleaned out of your hair. Now the next step is going to be Mitch Steady Grip. Now the great thing about this it's a gel and a lot of guys think that gel is just something you put in your hair and you go. For me, gel is the foundation of a style. So a lot of people have calyx, a lot of people have hair that doesn't want to stay up all day or they want to really fit their hair into a style. So what I'm going to do with Toby is I put gel in as my base. I blow it dry, get work out all of the calyx and kinks that helps give me that hold to do that. Also gives me a really nice clean look. So notice that I'm using the Palmichel 413 brush. I'm also using the Palmichel Neuro blow dryer and just working my way through blow drying that style in. And then once I have the style in, then I'll go in with my finishing product which is going to be the Barber's Classic. So this is a moderate hold. It's got a really nice shine to it.  So I go through it adds the shine. So because I blew it dry, we didn't have as much shine in the hair. So it gives me the shine. I already have the hair place where I want it. This is just the finishing touches. Gives a little bit of separation. Like I said, the shine. And then I'll go through and I'll comb the hair into the set style. So he likes to have that nice clean parting in his hair and also that definition. So I really like the weight line that we build up on the side. Nice blend through the opposite side. I like the balance of the haircut. You can still see a little bit of a weight line, but I personally really like that as long as it's a consistent weight line throughout the haircut. It's done on purpose. But this is the end result. Hope you guys like it. Let me know in the comments below. All right, guys, I hope you liked this video. If you did, hit the like button below. Also, subscribe to the channel. I got new videos coming out all of the time. And if you liked any of the tools that you saw, the scissors, colms, clips, anything like that, then check them out on freesaloneducation.com. Use the code YouTube. You get a special little discount there. And also, if you want any of the Paul Mitchell products, check out PaulMitchell.com. You can get more information on those and also to purchase. Go to a Paul Mitchell salon near you. Thank you guys so much for watching the video. I'll see you guys on the next one. Thanks.


All right, I am back again. Next up, we have a talk from Aleandro, who's going to be talking to us about production machine learning monitoring. Essentially, how do we deal with outliers, drift, and the overall statistical performance of the model. Great to have you here, Aleandro. A bit about Aleandro, he is the Chief Scientist at the Institute of Ethical AI and Machine Learning. He is also the Director of Machine Learning at Selden Technologies. We're really psyched to have you here, Aleandro. The stage is all yours. Awesome. Thank you very much. Today, we're going to be delving into a really interesting topic, the area of production machine learning in the context of monitoring production machine learning systems. There's quite a lot of content to cover. We're going to pretty much dive into very high-level areas, but in this link, that will be available throughout the presentation. You can access the blog post, which actually contains the code examples and the open-source frameworks and the references that you will be able to test everything yourself in your own spare time. A bit about myself, my name is Aleandro. I'm the Engineering Director at Selden Technologies and Chief Scientist at the Institute for Ethical AI. I'm also a governing Council member at large at the ACM. Selden itself, we are a machine learning, deployment management, monitoring, and explainability open core framework. We have a broad range of different open-source technologies as well as our enterprise product that builds on top of that. The Institute is a volunteer-led organization, research center-based in the UK that focuses on developing resources that ensure the responsible development and operation of AI systems. Now, let's dive specifically into this topic. What we're going to be covering today is the motivations for this, some principles that you can adopt, some patterns that abstract some best practices and some handsome examples. One thing to mention is that we are going to be exploring this from the perspective, not just of how can we deploy and monitor a model, but how can we actually enable this monitoring and more specifically advanced monitoring at scale with hundreds if not thousands of machine learning models. With that, as I was mentioning, you can find the blog post with all of the resources, so please do check it out. If you have any feedback, the projects are open-source, so you're able to actually provide any insights into that. Now, let's set the scene. We all are aware that production machine learning systems are hard. There's a lot of specialized hardware that is involved. There's a lot of complex dependency graphs. There's compliance requirements that go not just to the same level that software environments require, but now with the nuances of machine learning as well as the reproducibility nuances that you need to have in place. So from that perspective, it is important to take into consideration not just how you can develop and deploy those machine learning models to be able to have them in production, but how can you have more of a proactive as opposed to reactive interaction on the performance of the model and being able to address some of the issues that may arise such as performance drift as well as changes in the distribution of the data of your production models. So the key context to remember throughout this is that now it's more popular belief that the lifecycle of the model actually begins once it's deployed, right? It doesn't end when you finish training it is when you put it in production and it starts being consumed that the lifecycle really begins and you need to make sure that you have the infrastructure and capabilities in place in order for you to have the robust requirements from a more proactive perspective, the ability to know when you need to retrain the model, the ability to know when you have to identify and deep dive into a specific prediction as well as the general health of the models themselves. So what we're gonna be doing today is seeing how this applies into a model specifically. We're going to be deploying a single model as a microservice. We're going to be then able to see how this deployed model can't expose metrics that could be used for performance, for statistical performance, for outlier detection for drift detection and for explainability. So what we're gonna be doing today is we're gonna be taking the hello world of machine learning, this CFAR tank classification. This is basically a model that takes, as an input, an image and predicts one of 10 classes. In this case, it would be the image of a truck and it would predict the last class, which in this case would be a truck. So this is basically what our model is going to do. Given that we focus on the production lifecycle of the model, we're not going to be training a model from scratch, but the code is available if you want to do it. It takes a couple of hours, but we're going to be just fetching this ResNet32 CFAR 10 model. So it's basically a TensorFlow base model that we're going to be able to leverage with Python. We're going to be running the input being an image and then the output in this case is going to be an array showing us that it's the image, the class of a truck, right? The way that we're going to now productionize it is using this framework called seldom core. So seldom core allows you to convert or go from an artifact, right? So what we just saw, a model artifact and exported binary, like a pickled binary, or a Python, Java, or R code wrapper into a fully fledged microservice that can be deployed in Kubernetes and has a set of building blocks that allow you to actually connect into complex structures, like having multi-arm bandits, outlier detectors, feature transformations as part of your separate reusable components that in themselves are services, right? So the way that this is done is basically taking that artifact that we had or creating a class wrapper, which in this case is just a simple Python class with a predict method. Once we actually convert it into a full container using the source to image command line interface, we're going to basically have a component, a container that we can deploy and that will expose a REST, GRPC, and Kafka endpoint that will allow us to be able to perform inference on anything that we send. So whatever goes through this predict method basically is passed through the model and then is returned to us, right? Very intuitive. Now, the way that we're able to leverage cell and core is through the Kubernetes way, right? Defining your configuration schemas or your manifests. In this case, we are able to leverage some of the prepackage model servers. So in this case, it could be TensorFlow Serving, it could be Triton, it could be a scikit-learn model server or in this case, we're using the TensorFlow server and we're providing that binary as a Google bucket, right? So what we ended up doing was just exporting the artifact and then uploading it into a Google bucket so that we can then just provide this in this declarative way into Selden. So then Selden is able to basically take this and create the relevant containers, services, networking, et cetera. So now this basically becomes a fully fledged microservice. Now we are able to see this as the same inputs that we were running in our local model, we can now run it as a REST request, right? So we're sending a request to a predict method containing that input image and then our output is basically that class which is basically a truck, right? So we've deployed the model, but again, this is where the talk really starts, right? Because the point of the talk is deploying the model and then monitoring it, right? So what we're gonna now start looking at is the monitoring pieces, which you will be able to find a lot of relevant examples if you're interested to dive deeper into the Selden core repo in this open source. So please go ahead if you find an issue, you know, open an issue, that's the beauty of open source. So let's actually have a look at what is the anatomy of production machine learning systems, right? Because the lifecycle of a model starts from data to experimentation to deployment to monitoring, right? So the way that we can abstract it is by able to see that a data scientist will be either through their Jupyter notebook or through an ETL pipeline, they will be converting training data into the train model artifacts, right? This could be either that binary that we saw before or the container that we saw before. Then the next step is to be able to productionize it either through the manual CLI or by introducing a CI pipeline that automatically performs that deployment that allows us to have those deployed services which ultimately have inference data that passes through them, right? So any new data that you want to consume, you would pass it through the models. But where are the metrics, right? So the four key areas that we're gonna be delving into today, they're a little bit more, but these are performance metrics and tracing. So this is the same things that you would want to monitor for a microservice, statistical performance. So this is basically model machine learning specific metrics that you would want to monitor, drift detection and outlier detection for data that perhaps is wanted to be flagged being out of the distribution in one way or another. And then finally, explainability in the context of production models. Now, another reminder of the key thing that we said as a premise is that we are thinking about this not just from the perspective of how can we have monitoring for a model for a single model, but we want to ask the questions of what are the patterns that will allow us to perform monitoring at scale, to have hundreds of models with perhaps dozens of advanced monitoring components. Because you have to remember that if you deploy a model and then you add a drift detector, even though the drift detector is in a way reducing risk for you to be able to say, well, now I have that component to be able to flag any issues in a way you're introducing another machine learning component that equally requires monitoring in itself. So the drift component will also be a service that may fail, may also suffer from the same challenges that other machine learning components would suffer from. So it's something to take into consideration that we will be addressing as we talk about the different patterns in a more sort of like higher level perspective. So starting with performance monitoring, this is basically analogous to your machine learning microservice monitoring. So this is monitoring the performance of your running service, right? This is to identify potential bottlenecks in performance, debug and diagnose unexpected performance on that service. And this is really the usual things that you would imagine like CPU percentage, et cetera, which we will see in a bit. The way that you would see it from a sort of more abstract perspective is that once you deploy your model, one of the benefits of having this orchestration tools like sell and core is that not only it provides you with the endpoints to consume, but out of the box, it exposes those metrics that can be consumed by something like Prometheus. So you know if your model is suddenly perhaps having a memory leak, you may actually start to see that the memory starts to increase up to a point where it may crash. And then you can actually go and check why did that happen? Similarly, you can actually see some things such as the performance of the model itself given that a lot of the work that it's done is CPU intensive, right? So if you have worked with Python before, you know of the infamous GIL, right? So from that perspective, it is how you are able to optimize for the internal configurations of your model server such as minimizing in this case, the number of threads, making sure that the number of workers are aligned to the number of cores or well times two plus one, and also making sure that if you have too much, then it may actually end up with CPU throttling. So there's quite a lot of things to consider, but this monitoring of performance is the same or analogous to the one that you would expect to have in microservices. And from that perspective, it could be things like latency requests per second, CPU memory usage utilization, number of successful and failed requests, et cetera, et cetera. So these are equally important and still shouldn't be kind of like left off in your machine learning stack. The second point is statistical monitoring. So for statistical monitoring, we need to take into consideration now not just generic microservice metrics because even though it is still a production software system, we are still dealing with something that is much more nuanced. And in this case, the machine learning components will also require some further metrics that are gonna be very specialized. In this case, it would be perhaps something that may require the correct target labels, right?  And you're gonna be able to train a model because you have all of that data in there. However, when you deploy a model, you are not going to have those target labels, right? Because it's inference data. It's unseen data points. And what that means is that in order for you to be able to calculate things like accuracy, precision, recall, or other more specific ones like RMSE or several others, you may require having what we refer to as stateful metrics, right? Being able to collect those annotations that include the correct target label that may be provided in an asynchronous way, right? So once you deploy a model, you receive some requests, you may need somebody to actually go and re-annotate those requests. Then you will be able to start seeing some of those metrics like accuracy, precision, recall. Of course, there are others like KL divergence as well as perhaps some that you don't require that feedback or that correct label. But from that perspective, it does open some challenges of how do we achieve or how do we enable for this concept of feedback at scale? So the way that we have been able to address this with Seldom Core is that similar to how you have a predict endpoint that performs inference on top of your model, we actually also enable a feedback endpoint. And this feedback endpoint is able to use the unique request ID that you receive when you actually send an inference to allow other individuals to provide those correct labels, right? So you can do an inference request, you get told, okay, so this is request ID 55. Now somebody else later on can say, well, actually I'm gonna now submit a correct label for ID 55, right? So we can now see that from an architectural perspective, we now are able to abstract the concept of feedback as part of the models where the inference request as it comes, it gets saved into a, in this case, key value store. And when the actual feedback is provided, instead of actually being calculated inside of the actual microservice, this corrected label is actually forwarded into any other advanced monitoring components that want to do something with it. In this case, we have a component that just so happens to be listening to any feedback to be able to take that ID, fetch the inference request, make the comparison and update its current status so that you're able to then expose the specific stateful metrics through this advanced monitoring component, which we name as metric server. Now, some of the metrics that are provided wouldn't be just like the accuracy, right? Because you may want to actually have further granularity so that you can actually get, things like the accuracy per class or per subclass, you may want to get the precision and recall per subclass, et cetera. So from that perspective, you may want to capture the true positives, true negatives, false positives, false negatives, and individual prediction probabilities, as well as other things. Now, what a metric server looks like in practice, basically it's another sort of like abstraction that allows you to basically take the truth, the response and be able to calculate those particular components. So from that note, then you can see the intuition of abstracting the different servers and services so that you don't actually have to augment too much into the logic of your model itself and also to not affect the performance. Now, in regards to outlier and drift, it's similar in regards to the principles and the core concepts. Outlier detection is to be able to identify, you know, potential anomalies in your data and drift is for being able to identify a divergence in the data that you're seeing or the predictions. We are able to use another of our open source frameworks called alabyte detect, which has a broad range of different techniques that you can actually try out yourself and more importantly, deploy yourself. And in this case, we are able to actually show how we're able to leverage an outlier detector that we train locally, following the same pattern as we did with the model.  Right from this perspective, now we can see that every single request that it sends to your model, it's forwarded and it can be captured by any advanced monitoring component. In this case, it's an outlier detector, which is able to flag whether there's an outlier or not. And similarly for the drift detection, we follow the same pattern. In this case, a KS drift detector, we're able to train it locally and then deploy it in the same manner such that it performs the same pattern as the outlier detector, but instead of acting on a single data point, it acts on a window of data. So every, let's say, 100 or 1,000 of requests, it would calculate that data drift. So that's basically some of the core design patterns of the drift detection. And you have to take into consideration things like STOPE, whether it's supervised or unsupervised, whether it is a classification or regression task, as well as other things, but you can try all of this in the hands-on code that we provide as part of that blog post. Now, the last component is the explainability piece. And you may have heard about explainability in the context of Jupiter notebooks where you perform an explanation. But now remember that what we're seeing here is how do we achieve this at scale? How do we enable this human interpretable insights in a production environment? And we are able to still leverage the same component. In this case, it's our alibi that explain library, which has a broad range of different techniques like kernel shop, counterfactuals, anchors, et cetera, all which can be deployed. The only difference is that, similar to how the model receives a request and returns an inference response, the explainer receives a request, reverse-engineers the model for an intuition, and then returns the actual explanation. And there are some nuances to take into consideration how to choose your explainer, but again, you're able to delve into more details in the repo as well as in the example. This is whether your explanation is local versus global, where it's a white box versus black box. This means whether you actually will need the artifact of the model as part of the explainer, or whether you can interact with the separate services, as well as the data type, whether it's tabular, image, text, et cetera. Now, from an intuition, this is what it would look like from a microservice perspective. You would be able to actually say, well, for my model that predicted that this is a truck, what are the specific reasons why it predicted that it was a truck? In this case, using the anchors explainer, it basically tells you all of the anchors that made this image at least the most sort of like importance to be predicted as a truck. Now, there is a side note about things that we're not gonna cover in this talk, things like adversarial detectors that are another important thing and are also covered in the alibi-detect library. The actual ensemble patterns that you can also leverage is something that we're not gonna cover, but you can go into more detail. This is basically the ability to have these architectural patterns building upon each other. So a model that has a metric server and a drip detector that actually acts on top of the metric server instead of just the model. But this is basically just emphasizing the power that these architectural patterns provide, not just the abstraction, but also the flexibility that they introduce. So you need to take into consideration all the things, like alerting, what do you want to do when drift is detected, defining the service level objectives or several level indicators, as well as how you feed that into your promotion strategy and being able to drill down into any of the results that you provide. But again, this would go well beyond the scope of this talk and the scope of this talk is already very, very large. So I think that is a good point to stop on. Today we covered the motivations for machine learning monitoring. We covered some principles for some efficient approaches, as well as some scalable patterns that you can adopt that actually work at massive scale, as well as the hands-on example, which you can find in the code provided. With that, thank you everybody. And if there's time for questions, I'll be able to answer. If not, then we'll be able to cover them offline. So thank you everybody and I hope you enjoy the conference. All right, what a lovely talk. I think you were able to simplify a lot of concepts and if someone like me could understand it, then I'm pretty sure a lot of participants would have been able to understand it. So again, thank you so much for the lovely talk. I am right now looking to see if there are any questions. Someone's typing something. So maybe we give it a second. All right, there's one question. I'm just gonna copy it and put it up. Oh, I can see it, I can see it. Yeah, I'm just gonna put it up on the big screen if... Should I read it out loud? Yeah, so it is, what would you do if a model starts to drift but the model takes days to retrain? Would you keep the existing one in production until the new one is retrained? Oh, I mean, that's a great question and that's very much a coordinate. Well, I wouldn't say a coordinate case, probably I mean something super common as well. I mean, unfortunately, when there's a time constraint, there's a time constraint, right? Like if it takes days to retrain and you found out that there's drift, then there would be kind of like an assessment of what that drift really means. And that's actually something that our data science team is working on is around the question of, perhaps not all drift is bad, it is the question of like, which drift is the one that would perhaps have certain implications and the interpretability of drift that is flagged. But in your context, I mean, there's a lot to consider. It's basically what is the risk of keeping the model running based on what that drift means in particular. So yeah, I mean, not a very sort of like, black and white answer, but yeah, I mean, fortunately, some of these libraries and some of these patterns should at least provide you with a core foundation to tackle those challenges. All right, I hope that answers the question of the person who asked it. And someone said that this is the best talk of the conference, so congratulations on that. Well, I really appreciate it. These are the best attendees of the conference. Well, I don't know. Fantastic. So in that case, if anyone has any questions, then they can probably find you in the breakout room of Barrett and thanks again for your talk. Thank you very much. All right.