Making Data Simple: Inside machine learning with Steve Moore and

How does Artificial Intelligence (AI) come into play on a day-to-day basis? In this episode of Making Data Simple, Jean-François Puget, distinguished engineer, machine learning and optimization, and Steve Moore, senior story strategist for Inside Machine Learning on Medium, join host Al Martin to talk about machine learning, AI and more. A leading expert in his field, Puget discusses how to differentiate between machine learning business problems and optimization issues and how to get more value from your data.

Al Martin:             Hi folks, this is Al Martin from Making Data Simple, the series, if you will. Today I have Jean-Francois Puget. How’d I do? 

Jean-Francois Puget:   Yes, you did great. You passed your French test. 

Al Martin:                   All right, good, I’m going to give you the [name] JFP from now on, is that all right? So JFP is the distinguished engineer for machine learning and optimization, that’s the topic today and we’re going to go into that. I also have with me [Steve Moore], who is a senior content designer and storage strategist. Hey, Steve. 

Al Martin:             So Steve wanted to join the conversation, ask a few questions. So he’ll ask the intelligent questions, I will ask the normal, blockhead questions, if you will. So, thank you for being here. We’ve done a lot, well we’ve done at least, I think two podcasts on machine learning. We’ve done one on machine 1.15 learning for dummies, one for IBM machine learning, how to [help], if you haven’t heard those, go back, so we can’t do enough, and I notice that on your title JFP is machine learning and optimization. 

                             So I guess my first question for you is, you know, you’re one of the IBM experts, the evangelists for decision optimization, and particularly in the optimization part, what is it, and why should we be paying attention? 

Jean-Francois Puget:   All right. So people hear machine learning and deep learning every day. They have a good grasp, but it’s about predicting the future, or seeing the future from [today]. Optimization is moving one step futher, for instance, retail can use machine learning to predict demand for a product sales forecasting. You can have a good grasp on what your sales are likely to be in the next week. You can use optimization if you have good focus at your space. You can use optimization to manage your inventory, to know when to replenish, and manage your inventory at the lowest. So optimization is about making business decisions that improve given business goals without changing cost of inventory. 

Al Martin:              So that is to say you take the output of machine learning, and then optimization is simply making that and driving business value. 

Jean-Francois Puget:   Exactly, exactly. Taking advantage of what you know about the future, and plan accordingly. So planning scheduling, are use cases for optimization.

Al Martin:              Perfect, so that takes me to the next simple question: so what are you working on these days that aligns with that concept? 

Jean-Francois Puget:   We have a strange phenomenon coming now, especially in the ML Hub, they say “Oh, we want this machine learning, and here is our problem.” Nearly half of the time, their problem is an optimization problem and not machine learning. And we have to explain them, you look at the right goal, indeed, you have an interesting problem, we can help with you. But believe it or not, or rather believe it, what you need is not really machine learning, it is another analytic technology called decision optimization. So we need to find — I’m working, you know, on how can we capture this wave of business learning, thinking they need machine learning when they could use optimization instead. So that’s what I’m working on now.