At first glance, building and deploying machine learning models looks a lot like writing code. Tracking experiments in an organized way helps with all of these core issues. Weights and Biases (wandb) is a simple tool that helps individuals to track their experiments -- I talked to several machine learning leaders of different size teams about how they use wandb to track their experiments. The essential unit of progress in an ML project is an experiment, so most people track what they're doing somehow -- generally I see practitioners start with a spreadsheet or a text file to keep track of what they're doing. Spreadsheets and docs are incredibly flexible -- what's wrong with this approach?
Jun-4-2020, 16:29:57 GMT