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ML Experimentation - Fail Fast, Learn Faster

Experimentation is the foundation of machine learning and artificial intelligence model development. As William Blake said, ‘The true method of knowledge is experiment.’ 

An experiment is a way to answer the question, what happens when? Like what happens when I try this combination of chess moves? While the principles are the same, experimenting in the digital world of ML is very different than in the physical world. As a human being, I am limited in my ability to try out x number of combinations in a day, because I have to sleep and eat and rest and take mental breaks. But a computer never tires. It can crunch numbers all day and all night until it has tried every possible combination. In ML, there is the unique ability to more freely rely on computational power and big data to run thousands of simulations until some consensus is reached.

In this episode Saurabh and Melody talk to Alegion’s Chief Data Scientist, Cheryl Martin, all about experimentation in ML and how to fail fast to learn faster. 

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