Comet ML
By Comet
Comet ML is an MLOps platform for tracking machine learning experiments, managing model versions, and monitoring models in production, similar in purpose to Weights & Biases.
Definition
Comet ML is an MLOps platform for tracking machine learning experiments, managing model versions, and monitoring models in production, similar in purpose to Weights & Biases.
Overview
Comet ML lets data science and machine learning teams automatically log metrics, hyperparameters, code, and system information for every training run, then compare experiments side by side to understand which configurations produce the best results. This addresses a common pain point in machine learning development: keeping track of the many experiments run while tuning a model. In addition to experiment tracking, Comet ML provides a model registry for versioning and governance, artifact tracking for datasets, and production monitoring features that watch for model or data drift after deployment. It integrates with common frameworks such as PyTorch, TensorFlow, and scikit-learn. Comet ML competes directly with Weights & Biases and open-source MLflow in the experiment-tracking and MLOps space, and is commonly adopted by teams that want a hosted platform covering the full path from experimentation to production monitoring.
Key Features
- Automatic logging of metrics, hyperparameters, and code per run
- Side-by-side comparison of machine learning experiments
- Model registry for versioning and governance
- Production model monitoring for drift and performance
- Artifact and dataset versioning
- Integrations with PyTorch, TensorFlow, and scikit-learn