Machine learning is as accessible as it has ever been, but it’s not always obvious how to go from a cool paper to serving production traffic. Ben Linsay helps you get started putting your paper into production, sharing lessons learned solving real problems with machine learning at Kickstarter.
Ben covers tips for identifying a solvable problem, picking a dataset responsibly, running offline experiments, and not getting stuck in the prototyping phase before outlining the infrastructure that worked for Kickstarter, which ranges from a Ruby gem to a two-tier distributed system. Along the way, Ben highlights the trade-offs made, emphasizes the importance of measurement at each step, and discusses why focusing on a specific problem to solve and being diligent about measurement led to success.
Ben Linsay is an engineer. In past lives, he was an engineer at Bumpers, Kickstarter, Aggregate Knowledge, and Boundary.
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