Comet ML

Comet ML is a machine learning experiment management and model monitoring platform that helps data science teams track, compare, optimize, and reproduce their ML experiments throughout the model development lifecycle. Founded in 2017 and headquartered in New York City, Comet has established itself as a robust alternative to MLflow and Neptune.ai, with a particular focus on providing a smooth, developer-friendly experience for experiment management combined with production model monitoring capabilities. Comet's experiment tracking features enable data scientists to log hyperparameters, metrics, model graphs, code, system metrics, and custom artifacts with minimal code instrumentation. The platform provides powerful comparison tools including parallel coordinate plots, diff views, and interactive dashboards that help teams quickly identify the best-performing experiments and understand parameter sensitivity. Comet's automatic logging feature reduces setup friction by automatically capturing environment details, git metadata, and framework-specific information without explicit logging calls. A distinguishing feature of Comet is its model production monitoring (MPM) capabilities, which extend the platform beyond experiment tracking into production ML operations. MPM enables teams to monitor model performance metrics, detect data and concept drift, track prediction distributions, and set up alerts for model degradation. This connection between development experiment tracking and production monitoring creates a more complete lifecycle view that supports governance objectives around ongoing model oversight. Comet also provides a model registry for centralized model management, versioning, and lifecycle transitions, along with collaboration features including model review dashboards, commenting, and team workspaces. The platform supports integration with major ML frameworks (TensorFlow, PyTorch, scikit-learn, Hugging Face), notebook environments (Jupyter, Google Colab), and CI/CD tools. Comet offers a free community tier for individual users, with paid plans for teams and enterprises that include advanced features like model monitoring, SAML SSO, and audit logging. However, Comet's community and ecosystem are smaller than MLflow's, and its governance capabilities, while more comprehensive than pure experiment trackers, still fall short of purpose-built AI governance platforms. Enterprise features including advanced access controls and compliance features are limited compared to larger MLOps platforms.