Comet’s ML platform gives you visibility into training runs and models so you can iterate faster.

In addition to the 30+ built-in visualizations Comet provides, you can code your own visualizations using Plotly and Matplotlib.

Knowing what data was used to train a model is a key part of the MLOps lifecycle. Comet Artifacts allows you to track data by uploading directly to Comet’s machine learning platform or by storing a reference to it.

Comet Model Registry allows you to keep track of your models ready for deployment. Thanks to the tight integration with Comet Experiment Management, you will have full lineage from training to production.

The performance of models deployed to production degrade over time, either due to drift or data quality. Use Comet’s machine learning platform to identify drift and track accuracy metrics using baselines automatically pulled from training runs.

Track your LLM prompts and responses in Comet to keep a single system of record for all your Prompt Engineering work. Add token metadata, benchmark the performance of different LLMs, and score prompt responses to find the best prompt templates for your specific use-cases!