Why choose Comet?¶
Comet’s self-hosted and cloud-based machine learning platform allows data science teams to track, compare, explain, and optimize their experiments and models across the complete ML lifecycle – from training runs to production monitoring.
Data scientists and ML engineers choose the Comet platform because it has the flexibility required for the most iterative data science teams, and it is built to handle the intense demands of enterprise ML at scale.
The Comet difference¶
While some ML platform vendors offer stand-alone experiment tracking or model production monitoring systems, Comet offers both. By bringing these two together, we can provide significantly more value to users and customers.
Flexible - Run Comet’s platform on any infrastructure, including on-premises or virtual private cloud (VPC) installations, with dozens of installation flavors. Bring your existing software, data stack, and authentication system, whether you use Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform, or your own servers.
Easy to start - Add two lines of code to your script, notebook, or pipeline. The next time you run it, Comet’s software development kit (SDK) will transfer everything you need to track and manage code, metrics, and hyperparameters.
The Comet platform supports every stage of the machine learning lifecycle, from tracking training runs to monitoring models in production.
- Experiment tracking and management - Track training runs and models, with easy reproducibility for experiments.
- Dataset versioning - Track and store datasets with Comet Artifacts.
- Model registry - Keep a library of trained models in Comet Model Registry.
- Model production monitoring - Monitor model performance and identify drift.
- Code panels - Create customized visualizations for faster iteration.
- Reports - Share results, collaborate across teams, and measure team outputs.
- Putting machine learning models successfully into production
- How scientists at Uber use Comet to manage machine learning experiments