This post is the first in a 3-part series. What do machine learning practitioners do? (Source: #WOCinTech Chat)
JEFF DEAN GOOGLE KEYNOTE HOW TO
In thinking about how we can automate some of the work of machine learning, as well as how to make it more accessible to people with a wider variety of backgrounds, it’s first necessary to ask, what is it that machine learning practitioners do? Any solution to the shortage of machine learning expertise requires answering this question: whether it’s so we know what skills to teach, what tools to build, or what processes to automate. I follow these issues closely since my work at fast.ai focuses on enabling more people to use machine learning and on making it easier to use. In his keynote at the TensorFlow DevSummit, Google’s head of AI Jeff Dean estimated that there are tens of millions of organizations that have electronic data that could be used for machine learning but lack the necessary expertise and skills. There are frequent media headlines about both the scarcity of machine learning talent (see here, here, and here) and about the promises of companies claiming their products automate machine learning and eliminate the need for ML expertise altogether (see here, here, and here).
Part 2 is an opinionated introduction to AutoML and neural architecture search, and Part 3 looks at Google’s AutoML in particular.
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