Now you can easily find and organize your experiments with filtered views based on experiment metrics, metadata, and parameters
Machine learning teams often work with many, many models and their iterations (often in the hundreds and thousands 📈). We received overwhelming feedback from our users about needing improved discoverability for their machine learning experiments.
To enable our users to better sort through and discover their most interesting and highly performing experiments, our team implemented a feature called the Query Builder.
With the Query Builder, Comet.ml users are able to do more complex and effective experiment filtering so you only see relevant experiments.
A Closer Look:
Let’s do a quick walkthrough of how the Query Builder can help you and your team. You can try the Query Builder today by signing up for a Comet.ml account!
Making a Query
With the query results, you can now easily see what your top-performing experiments are (ex. AUC score > 0.95) and sort even further within the subset of experiments.
💥 Note: all of our filters are
AND operators for now
You can also save a set of filters as a Saved Query so that you can access the same subset of experiments again or see what new experiments match the filters you set.
💥Pro Tip: if you’re on a Teams Comet.ml account, your Saved Queries will also accessible to your teammates who are on the same project!
Combining Query + Group By
To kick it up another organizational notch, you can also combine the Query Builder with our new Group By feature. This allows you to group the filtered subset you already have (made with the Query Builder) in yet another level. Very useful for organizing cross-validation runs!
Comet.ml’s Query Builder allows for exponentially improved experiment discovery, transparency, and
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About Comet.ml — Comet.ml is doing for ML what Github did for code. Our lightweight SDK enables data science teams to automatically track their datasets, code changes, experimentation history. This way, data scientists can easily reproduce their models and collaborate on model iteration amongst their team!