Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models.
Add a single line of code to your notebook or script and start tracking your experiments. Works wherever you run your code, with any machine learning library, and for any machine learning task.
Easily compare experiments—code, hyperparameters, metrics, predictions, dependencies, system metrics, and more—to understand differences in model performance.
View, analyze, and gain insights from your model predictions. Visualize samples with dedicated modules for vision, audio, text and tabular data to detect over-fitting and easily identify issues with your dataset.
Build custom visualizations based on experiments and model data, or use community provided ones. Debug, explain and visualize with no limits.
I've spent a good amount of time evaluating Comet's competitors and alternatives and concluded that Comet is the best solution. None of the other products have the simplicity, ease of use and feature set that Comet has.
Comet provides a central place for my team to track their ML experiments and models so we can seamlessly compare and share experiments, debug and stop underperforming models. Comet has improved our efficiency as data scientists and as a team.
Doing ML with Comet is like building with legos. You can customize and combine your data, code, visualizations, reports, and much more to create exactly what you want.
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