Comet is a commercial platform for experiment management, model monitoring, and reproducibility in machine learning workflows. It’s designed for data scientists, ML engineers, and research teams who need to track, compare, and evaluate model training runs, parameters, and results across the entire lifecycle of AI development.

Focused on improving visibility and collaboration in experimentation, Comet enables users to log key metrics, monitor training in real time, compare models, and manage artifacts for future reuse. It supports integration with popular ML libraries and tools, and offers enterprise features for large-scale AI experimentation environments.

Key benefits:

• Centralized platform for tracking, comparing, and managing ML experiments

• Enables reproducibility, version control, and auditability

• Scales to support collaborative, production-grade model development

What are the main features of Comet?

Experiment tracking and metadata logging

Comet provides comprehensive tracking of all components in an ML experiment:

• Logs parameters, metrics, hyperparameters, datasets, code versions, and outputs

• Supports real-time logging and visual dashboards

• Compatible with frameworks like TensorFlow, PyTorch, XGBoost, Scikit-learn, etc.

• Enables automatic saving of custom metrics and artifacts

Model comparison and performance analysis

Allows users to understand how different runs and configurations affect outcomes:

• Compare multiple experiments side by side

• Visualize loss curves, accuracy trends, and evaluation metrics

• Track changes across versions of models and pipelines

• Annotate and document findings for reproducibility

Team collaboration and shared workspaces

Facilitates coordinated work across ML teams:

• Shared dashboards and experiment libraries

• User access controls and project-level organization

• Discussion and annotation tools for collective review

• Helps maintain consistency and transparency in model development

Artifact management and versioning

Ensures that code, data, and model files are stored and versioned properly:

• Store and version datasets, scripts, checkpoints, and model outputs

• Trace any result back to its exact configuration and environment

• Makes it easy to rerun, audit, or extend previous experiments

• Supports long-term governance and compliance tracking

Integration with MLOps pipelines

Fits into existing ML workflows and infrastructure:

• Works with Jupyter notebooks, CLI, Python APIs, and CI/CD tools

• Integrates with Kubernetes, Git, MLflow, S3, and more

• Exports data for use in dashboards or third-party tools

• Enables seamless flow from experimentation to deployment

Why choose Comet?

• Full lifecycle experiment tracking for machine learning projects

• Reproducible and auditable experiments with clear version history

• Collaborative tools to support team-based model development

• Flexible integrations with popular frameworks and cloud services

• Designed for high-scale experimentation environments