 Okay, good morning everybody. Welcome to the fifth edition of the fifth elephant. It's not a pun It's a reality and you've made it possible by coming here Some of you have been coming here year-on-year Some of you have attended past editions and this is your second or third edition. So welcome back Really appreciate your support for the conference and your participation. I Just quickly like to walk you through The logic of the schedule today and also raise a bunch of questions that I hope we will talk about During the conference over the next two days These are questions which I believe will be fairly pressing in most of your minds and I'm sure there will be more questions That will come up and we'll have a lot of opportunities to share them. So, you know, what's happened in the last five years? With with the scene with data with machine learning with analytics I think we started off in 2012 with the fifth elephant talking about big data And I think at that time it was the buzzword and it was the hype by the time we reached 2014 There was a realization that you know big data is really not that big unless it's health and telecom sectors But there are there's a fair bit of data that a lot of organizations are dealing with there are organizations of the scale of LinkedIn there are organizations of the scale of the curtain and there are startups all of who have data and Everybody wants to know what to do with the data how to analyze it how to make meaning for fit So we decided to drop this whole idea of big data and focus more on how do you analyze this data? And by the time we got from 2014 2015 It was all about machine learning. How do you train models? How do you train your machines to make sense of this data to be able to throw some meaning out of it? And to be able to throw meaning and information out to users and now we are here in 2016 where we're talking not just about machine learning We're also talking about deep learning and artificial intelligence We thought of forced us at the fifth elephant to do a completely separate conference on deep learning earlier this month And this will be a trend that we hope will continue next year as well to have more focused conversations I think there's also been a question about how much technology itself has progressed in the last five years Right from the time of big data technologies to spark which was at a big height last year The question really now is what are the technologies that we're looking forward to this year in the coming years? Is the Apache Foundation going to give us some more answers? Where are the answers going to come from? Are organizations investing still in building their own technologies or is the problem lying somewhere else? I'd also like to briefly mention how the conference itself has progressed over the last five years and the kind of The way in which we've developed content for the conference We started in 2012 by asking the community to submit talks and selecting talks on the basis of votes and upvoting To a time in the two years later between 2013 and 14 where we decided to Literally curate the talks as a tight-knit editorial panel of five or six members We just sort of select the talks whether the votes were up or down We'd make a decision based on our better judgment to a time from 2015 2016 Where we've had now conference attendees who come forward to help us collaborate and build the conference this year Believe me or not. There are 10 collaborators who've made this conference possible and all of them are volunteers One of them is sitting among you Vishal go clay who was an attendee last year And this year has been instrumental in helping us get our first ecology talk and also our statistics lesson That's going to happen today on the MCMC algorithm. I think this is an open invitation now going forward that the audience has To collaborate in producing the content that you'd like to see at the conferences next year So I think there's a there's been a remarkable movement in the audience itself. That's a big cheers to you I think Though what are the challenges that lie ahead of us the questions really are really about do we Do we I think the the challenge at first the question that sort of That's biggest on everybody's mind is what is the product that you build out of your data? And I think that's the question we are looking to answer in the next two days over here We're looking to understand from the wisdom of speakers from all these domains of health care Genomics finance etc to understand, you know What is it that you can actually go ahead and do with the data here? And I'm sure that not all of them have all the answers some of them are also in the exploratory Species, but I suppose that's the way to kind of get to the answer the question to the From question to answer is the journey and we are hoping that this conference helps in in accelerating that journey I think the other question is do we really require in-house expertise for data analytics or is this something that can be outsourced I Have gene over here who's going to be doing the keynote today He's going to be here at the conference today and tomorrow and I think that's one of the questions to look up and ask him as well And as well as some of the other speakers here And I think the last pressing question is how do we bridge this gap between academia research and practice? In the last month when I was in the US talking to gene and and going around in universities It's very apparent that university professors are also part of Organizations like Google Amazon, etc. And they are actually practicing Programmers developers machine learning scientists Deep learning scientists over there. We don't have that the gap is pretty wide in India There is still a pretty large gap between what happens on the research side and what happens on the practice side And we've tried to bridge this gap this year the fifth elephant by actually bringing people from the ISI to come and talk over here I can sense how Apprehensive and excited they are to talk to a practitioner audience and I believe this is the first way forward to bridge this gap And I think we need to do a lot more to be able to bridge this gap between University and practice and that's an honest that lies on all of us at the same time How do you build more capacity to develop data scientists? How do we help more programmers to cross the deep and to make the paradigm and the resource shift from learning coding? And also doing math and statistics. I think that's a challenge to look forward to And I think there are a lot of these questions that we're going to ask so very quickly I'm not going to take up too much time I'll just help you navigate the schedule today and then tomorrow when we summarize I have you navigate the schedule tomorrow in hall 1 today What we've done is put all the application talks over here So you have starting with a mission stock on healthcare and then Jean and really going into talking about finance and fintech Aditya Karnik who they don't talk about sensors and what they're doing at Zen drive with with data from sensors and And human beings you'll find a lot of these diverse application talks in auditorium one today And of course we'll conclude with our statistical class today By some and they will talk about the the most popular algorithm of the 20th century the mark of chain Monte Carlo algorithm So that will be an interesting session to look forward if Track 2 has most of the e-commerce talks happening today Starting with Aditya Rakhachand Rakhachand who's going to start at 9 35 a.m talking about The forecasting and advertising problem and how they're solving it at flipkart machine learning and then subsequently a bunch of talks by Vijay Gabley Ashish Kulkarni and others who will be talking about problems like Catalogging and other issues that are there in e-commerce and then of course we have a bunch of other diverse talks in auditorium 2 with Pankata Pingalil talking about Versioning problems and integrity problems with data and Shaurya Rai coming in to talk about transfer learning techniques So so that's happening in auditorium 2. So choose your talks. Why is the today choose your tracks? We also have a bunch of words of feather sessions One on on maths and statistics. What does it take for programmers to cross the divide? So hopefully people over here who are coding would like to You know be part of the discussion Please join the birds of feather session auditorium 3 all the auditoriums are on the same floor the balcony for auditorium 1 is on the top floor And we have a second bof session on alternative data gene who's here is the author of Of the term alternative data And the idea of the birds of feather session is to be able to talk and understand the whole concept of primary data What does it take to build products out of primary data? Why is primary data more interesting than massage data? And I think a lot more interesting and relevant questions I'm hoping also people like Udit Podar, Riddhi Mittal will be able to join the session and and contribute from their Contribute from their experience. I'm going to stop now, but please turn off your phones or put them on silent It is really Not good courtesy to be talking on your phone while the speaker is speaking