Why is experiment tracking so important for doing real world machine learning?
At first glance, building and deploying machine learning models looks a lot like writing code. But there are some key differences that make machine learning harder:
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? See more at Machine Learning Experiment Tracking.
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We're building lightweight, flexible experiment tracking tools for deep learning. Add a couple of lines to your python script, and we'll keep track of your hyperparameters and output metrics, making it easy to compare runs and see the whole history of your progress. Think of us like GitHub for deep learning.
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