Spend More Time on ML, Less Time on Ops (Presented by Weights & Biases)

As a machine learning (ML) practitioner, have you ever built an amazing model but couldn’t reproduce it for a colleague? Maybe you've questioned why your model is making strange predictions downstream? Is it difficult to explain to others the impact of what you’re doing? In traditional software, we solve this problem with the git “diff” to see what code changes led to strange behavior. Can we do the same for machine learning? Yes! Learn the best practices for setting up your experimentation pipeline to easily and systematically track, compare, and share experiments over time so you can spend more time on ML — and less on ops.