Running a Successful Machine Learning Startup

08/10/19 • 26 min

Today, our guest, Alain Briancon, will talk to us about how to work with Fortune 500 companies and help them get quick value from their data, how to build a roadmap of incremental value during the data collection and analysis process, how they help predict and incentivize customer purchases, and how to dial in on an idea for successful data science software companies.Alain Briancon: Adding one more question to answer is always easy. The difficult part is what question can I remove and still providing insight.Ginette Methot: I'm Ginette Curtis Seare: And I'm Curtis Ginette: And you are listening to Data Crunch Curtis: A podcast about how applied data science, machine learning and artificial intelligence are changing the world. Ginette: If you’re a fortune 1000 company, and your team needs to be trained in Tableau, Statistics, Data Storytelling, or how to solve business problems with data, we’ll fly one of our expert trainers out to your site for a private group training. The most important investment a business can make is in its people, so head over to our site at datacrunchcorp.com and check out our training courses.Today, our guest, Alain Briancon, will talk to us about how to work with Fortune 500 companies and help them get quick value from their data, how to build a roadmap of incremental value during the data collection and analysis process, how they help predict and incentivize customer purchases, and how to dial in on an idea for successful data science software companies.Alain: My name is Alain Briancon. I am currently the VP of data science and chief technology officer for CEREBRI AI. CEREBRI AI is an AI company, as the name could guess. We are located in three cities: Austin, which is the corporate headquarters; Toronto, which is a hotbed of data science in North America; and Washington DC where I work. What CEREBRI AI focuses on is developing a system to help manage above the strategic component as well as the tactical component of customer experience. This is my fifth startup. This is my third startup that involves data science and machine learning. Jean Belanger, who is the CEO of CEREBRI is a friend of mine; now he's my boss. So I'm trying to work through that, and it took him about 19 years to convince me to join a startup, uh, with him. And this was the right opportunity because the kind of problems we are solving are very challenging.It has been a, an absolute blast. Besides working with a great team and building it up. But when I joined we were about 20 people. Now we're about 63 people, about 50 of them on the technical side. Half in data science, half in software. What has been fantastic is applying tricks and insight that I've gained over the years to, uh, help guide the data science side. The other thing also, which is fun, is we have a very pragmatic view of how to approach things and how to approach engagement with customers. Our customers are fortune 500 customers; they are major banks. One of them is a Central Bank. Others are car makers and we're working very hard into the telco business as well. And, uh, when you deal with such companies, first of all, a very interesting sell cycle in which data science and machine learning play a role at the right moment in time.But you have to also be humbled by the fact that you don't start on their side from a clean sheet. And I think that's one of the most interesting component of making things work is bring data science and machine learning insight to companies who cannot afford and we should not afford the, "okay, let's start from scratch. Let's share all of the data in the like," and so vis Jujitsu between the business case that machine learning brings up and the underlying machine learning technology is one of the most fun element of the work.Curtis: That's interesting. Let's, let's dig into that if we can. Can you give me a concrete example in CEREBRI AI how that works and spell out that concept for us?

Previous Episode

Executive Panel: How Can Data Science, ML, and AI Best Support Executive Goals

Today is a special episode. We welcome three executive guests from different organizations to share their experiences and insights about how data science can best support executive goals. Ginette Methot: I'm Ginette Curtis Seare: And I'm Curtis Ginette: And you are listening to Data Crunch Curtis: A podcast about how applied data science, machine learning and artificial intelligence are changing the world. Ginette: Data Crunch is produced by the Data Crunch Corporation, an analytics training and consulting company. There's a lot going on here at Data Crunch. Just this last week we finalized the merger of Vault Analytics and Lightpost Analytics under the new banner of the Data Crunch Corporation, which improves our capabilities to serve our clients head over to datacrunchcorp.com to check out our training and consulting offerings. For our executive panel, today we'll be talking to Simon Lee, the chief analytics officer from Waiter; Fatma Kocer, who is the vice president of data science engineering at Altair, and Rollen Roberson who is the president at Trianz. Curtis: So, welcome everyone to the executive panel. We are super excited to have you guys here. You are all executives and companies that are doing amazing things with data science. So the audience knows, again, we're talking about today, the topic is how data science, machine learning and AI can best support executive strategy and business goals. How, how does that function really work? Let's start maybe with Simon and then Fatma and then Rollen, if you could just give us a little introduction, and we'll get going from there. Simon Lee: Thanks. I'm Simon Lee. I'm actually kind of a mixed bag when it comes to data science and analytics. I've got about 20 years of experience using analytics and advanced algorithms, you know, in a whole bunch of different industries like transportation for example, airline rail, trucking, ocean carriers, printing, publishing, manufacturing, finance and delivery. Delivery is where I'm currently at. Waiter is a restaurant, food delivery company in small and mid size market. So probably a lot of people haven't heard of us because we're in the smaller communities, but, we're trying to make a big splash. So yeah, that's who I am. Curtis: Awesome. Thanks for being here. Fatma Kocer: Hi, this is Fatma Kocer from Altair engineering. I am a civil engineer by training, although I never get a chance to practice it. Um, my background is multidisciplinary design, exploration and optimization. And I was in the auto industry before I joined Altair. Um, there, I've done several things throughout the 14 years that I've been here, but always keeping, designing solution optimization as the core of my responsibilities. And Altair is a global technology company. We provide software in solutions for product development, data intelligence and high performance computing. We are located at headquarters in Michigan in Troy, Michigan where I'm speaking from and we have offices in I think 25 countries now. So that would be me. Curtis: Great. Thanks for being here, Fatma, and, ah, Rollen. Rollen Roberson: Right. Thank you. Good Morning. Rollen Roberson with Trianz. You know, for my own background, I've a similar to Simon. I'm kind of a mixed bag, I've been in the industry for 20 plus years, I'm solely in the digital transformation space. Uh, working from startups, mid-level companies through global service integrators, uh, working with Trianz currently to really expand the growth and a use within AI and IoT within the organization. And our customer base, Trianz is a company that has 1,500 plus employees, global offices mainly serving the, upper, mid-tier and enterprise level customer base, uh, solely focused on digital transformation and the use of those higher technologies for greater return on value. How Can Data Science and AI Have an Impact On Your BusinessCurtis: That's awesome.

Next Episode

Last-Mile Logistics Analytics—for Everyone Who Isn't Amazon

Today we speak with Professor Ram Bala, an expert in supply chain management analytics, particularly last-mile delivery. He has very interesting insights into how today’s supply chain is evolving. He talks about various methods and algorithms he uses, the specific challenges inherent in doing last mile logistics and deliver, how pricing factors in, and how everyone is trying to catch up to Amazon.Ram Bala: Then there is this great opportunity to actually use the data effectively. But that is a long way to go in terms of coming up with the right algorithms, both on predictions, as well as the optimization to actually get this done in a meaningful way. And if you look at the landscape today in terms of industry, I would say very few companies that actually there yet. Right? I mean, Amazon obviously is a clear example of the leaders in the space, but everyone's trying to get there as well.Ginette Methot: I'm GinetteCurtis Seare: And I'm CurtisGinette: And you are listening to Data CrunchCurtis: A podcast about how applied data science, machine learning and artificial intelligence are changing the world.Intro: Today we speak with Professor Ram Bala, an expert in supply chain management analytics, particularly last-mile delivery. He has very interesting insights into how today’s supply chain is evolving.Ram Bala: My name is Ram Bala. I'm a professor at Santa Clara University as well as a data science leader at CH Robinson, which is the largest logistics marketplace in North America. I've been working with topics in supply chain, belated data science even before it was called data science for the past 15 years. I got my Ph.D. in operations research and a supply chain from UCLA and a, I've been working on these problems both for companies as well as within the academic context that I've been working on research problems. And more recently I think there's been a lot of excitement in this space. And then that's where my involvement with both startups and as well as larger companies has gone up and I, I came into the CH Robinson fold as a consequence of an acquisition. So I was part of a startup that was working on last mile logistics and how to, how to improve that.Curtis Seare: Got It. That's awesome. And the space that you're in is really interesting. Could you give the audience just to contextualize the problem set that you're focused on?Ram: So I think one of the major things that has changed in logistics is the growth of e-commerce and also personal mobility. I mean if you think about Uber Logistics as a larger concept that covers both moving people as well as products and what's really happened is the, the availability of real time data has had a significant consequences on how we are able to predict as well as optimize how we move things and that's then also raised the bar in terms of customer expectations. We expect to get a get a ride to go somewhere within and within five minutes, we expect to get a product within a day and those expectations have been set by specific companies say Uber in the case of personal mobility. In the case of products, it's Amazon and having set the stage, everyone's now trying to be competitive with them, which means that in the product space, certainly all e-commerce companies as well as companies that were in brick and mortar are trying to achieve that same end goal, which is how do I get products to consumers quickly at the same time and not spend too much money? Right? That's the core problem. Now doing that as hard, it's become easier simply because we have real time access to real time data in terms of location as well as you know where products are at an even point. But it is a hard problem to solve.Curtis: Some of the intricacy and you know, routing and pricing and kind of interplay there. Can we dive into a little bit of those details?Ram: Absolutely. So I think uh, routing problems have been around ever since transportation's been around,