Machine learning drove massive growth at consumer internet companies over the last decade, and this was enabled by open software, datasets, and AI research. For many problems, machine learning will produce better, faster, and more repeatable decisions at scale. Unfortunately, building and maintaining these systems is still extremely difficult and expensive. As more machine learning software moves to production, many of our traditional tools and best practices in software development will change.
Pete Skomoroch walks you through what you need to know as we shift from a world of deterministic programs to systems that give unpredictable results on ever-changing training data. To navigate this world powered by nondeterministic data-dependent programs, we’ll also need a new development stack to help us write, test, deploy, and monitor machine learning software.
Pete Skomoroch is the former head of data products at Workday and LinkedIn. He’s a senior executive with extensive experience building and running teams that develop products powered by data and machine learning. Previously, he was cofounder and CEO of venture-backed deep learning startup SkipFlag (acquired by Workday in 2018) and a principal data scientist at LinkedIn, the world’s largest professional network, with over 500 million members worldwide. As an early member of the data team, he led data science teams focused on reputation, search, inferred identity, and building data products. He was also the creator of LinkedIn Skills and LinkedIn Endorsements, one of the fastest-growing new product features in LinkedIn’s history.
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