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Comet Launches Prompt Management Tools to revolutionize your Large Language Model workflow.

Full Transparency for ML Experiment Tracking

Get automatic experiment tracking for machine learning with tools to version datasets, debug and reproduce models, visualize performance across training runs, and collaborate with teammates.

Track, Compare, and Manage ML Models Using Your Current Workflow

Add two lines of code to automatically track, manage, and optimize models for faster iteration. Keep using the tools, libraries, and frameworks you use today for ML experiment tracking.

Deploy your way, using your requirements. We treat virtual private cloud (VPC) and on-premises environments as first-class citizens.

One Platform for the Complete ML Experiment Tracking Lifecycle

Compare code, hyperparameters, metrics, predictions, dependencies, and system metrics to understand differences in model performance. Introduce a model registry for seamless handoffs to engineering. Monitor models in production with a full audit trail from training runs through deployment.

Experiment tracking
Icon image with file folder and padlock
Model management and registry
Production monitoring

Best-in-Class Visualizations

Built-in Charts

Easily track training metrics in real time, compare performance, debug, and evaluate models faster with built-in code panels.

Create Your Own

Easily implement your own dynamic visualizations using Matplotlib, Plotly, or your favorite library with Comet Code Panels.

Train and Iterate Faster

Filters to Analyze Training Runs

Create filters for your experiments based on their attributes to support faster analysis and iteration during ML experiment tracking.

Fully Customizable Project Views

Collect your experiments in a project, where you can manage, analyze, share, and make notes on them.

Workspaces to Collaborate

Use your workspace for personal and public projects. Create team workspaces for easy collaboration.

Publish Models to a Registry

Save model versions from the best experiments. Manage deployment stages with tags and webhooks.

Easily track a model lifecycle and lineage from model binary, through experiment tracking to training datasets.

Compare the performance of models in production with their baselines in training.

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