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Really Automating Machine Learning at ML Prague 2018

by on February 20, 2018

On March 23-25, 2018, more than 1,000 Machine Learning practitioners will gather at Machine Learning Prague 2018 to hear from 45 speakers currently working in the Machine Learning field. The event will take place over 3 days with fun and interesting talks, such as “Really Automating Machine Learning”, presented by Dr. Charles Parker, BigML’s VP of Machine Learning Algorithms, who will present what’s behind the scenes of Machine Learning automation, focusing on how to know when your automation is succeeding, and what to do to make it easier. Interested in attending Dr. Charles Parker’s talk or other sessions at Machine Learning Prague 2018? Purchase your ticket today using the code below to get a 10% discount!

We interviewed Dr. Charles Parker to share a glimpse of what’s to come in his talk at ML Prague 2018. Find the excerpts below.

During the last years of your career, you have been focusing on ML automation. What’s the motivation behind this topic?

The main reason is necessity: the amount of training data available to people is increasing. As it does, the model complexity we can consider grows more and more, which means we need new ways of addressing parameterization of all of these very complex models. Nobody, experts included, has the time to figure out, for example, the effects of all of the various parameters of all of the various ways of doing gradient descent. So these automation methods are sort of bound to become de rigueur in the field.

On a personal level, there’s something compelling to me about working on a layer atop all of these parameters. The problem you’re trying to solve when you’re parameterizing an ML algorithm is “what are the best parameters for this algorithm for this data?.” At the automation level, your task is “what is the way to find the best parameters for any algorithm on any data, given finite compute power and time?”  To me, there are a whole bunch of interesting questions in there about the distribution of datasets in the real world as well as the distribution of loss functions. What kinds of data do “most people” have, and how to “most people” know when they’ve got a good model?  Knowing those priors would make automation a lot easier. However, not only do we not know what they are, but I don’t think we even have a very good idea about how to figure out what they might be. That’s an exciting place to work.

What would you tell those who refuse to join the Machine Learning revolution?

I’m going to surprise everyone and say those people are absolutely right to be suspicious! Machine Learning is harder than you think. Using ML algorithms is fundamentally a different way of interacting with computers than most people are used to, and it takes some time and talent to develop a facility for that interaction. When we train ML algorithms, we’re really programming with data. As such, even though you don’t need to write code, you need to develop a lot of the same habits of mind that a programmer has: Good programmers learn early on to see their code as the machine sees it; good ML practitioners see their data as the machine sees it.

In addition to all of that, it’s also harder to measure the performance of a Machine Learning algorithm than you think, so even when people have done everything right with their data, they sometimes still don’t get what they think they will.

This isn’t an excuse to let it pass you by, though. Forty years ago, computers themselves were difficult to use and strictly the domain of geek enthusiasts. Those who developed the ability to interact with them early on put themselves in a position to succeed in a whole bunch of different careers. I think it’s very possible that Machine Learning will have the same sort of transformative power for those who take the time to learn how to do it right.

You have been developing Machine Learning applications for quite some time. Based on your experience, what would you recommend to the new ML practitioners that are starting now?

The best way to learn is by practicing. I recommend to get some data and start playing around, trying to predict things, introspecting models. If that’s too vague, check out the BigML education videos and try to find data from other sources where you can do the same things we’re doing in the videos.  In this way, you get a sense of how Machine Learning data is “supposed to look” which is maybe the first and most important skill when you start with Machine Learning.

There’s this misperception that you have to know how to write code to get started and that just isn’t true anymore. BigML is a fantastic playground for messing with these ideas because you don’t have to write any code or install any software. On top of that, we’ve got an interface that lets you interact with the models you train, which is hard to come by elsewhere. Sure, it sounds like a pitch for BigML, but if I didn’t think it was the best way for non-experts to do Machine Learning, I probably wouldn’t work here!

About the Lecturer

Dr. Charles Parker is the Vice President of Machine Learning Algorithms at BigML. Dr. Parker holds a Ph.D. in computer science from Oregon State University, and was previously a research associate at the Eastman Kodak Company where he applied Machine Learning to image, audio, video, and document analysis. He also worked as a research analyst for Allston Holdings, a proprietary stock trading company, developing statistically-based trading strategies for U.S. and European futures markets.

His current work for BigML is in the areas of Bayesian Parameter Optimization and Deep Learning. One of his recent projects developed at BigML was bringing Deepnets to the BigML platform, the world’s first deep learning algorithm capable of Automatic Network Detection and Structure Search, which automatically optimize model performance and minimize the need for expensive specialists to drive business value. Dr. Charles Parker ran a benchmark test with over 50 datasets representing a variety of use cases and industries. Based on extensive evaluations that considered 10 different performance metrics, each based on 10-fold cross validation, BigML Deepnets came out on top of 30 competing algorithms.

Additionally, Dr. Charles Parker regularly contributes to the BigML blog. Here are a few highlighted for your leisure:

 

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