Detailed Introduction
ClearML is an open-source MLOps platform that covers experiment tracking, data management, pipelines, orchestration, scheduling and model serving. It uses lightweight agents and automated CI/CD capabilities to collect training logs, metrics and model snapshots, helping teams achieve reproducibility, model versioning and training observability for both cloud and self-hosted deployments.
Main Features
- Experiment tracking: automatically records run parameters, metrics and model artifacts for comparison and rollback.
- Pipelines & orchestration: built-in pipelines and scheduling for automated training workflows.
- Data & model management: store and manage datasets, model versions and artifacts.
- Deployment & serving: support packaging models for online and batch serving.
Use Cases
ClearML is suitable for research and engineering teams that need centralized experiment management and production workflows, such as experiment comparison, automated training pipelines, resource monitoring during training, and rapid promotion of trained models to inference services.
Technical Features
- Open-source license: Apache-2.0 licensed for easy integration and extension.
- Framework compatibility: integrates with PyTorch, TensorFlow, Transformers and other frameworks.
- Extensible agents: lightweight agents collect runtime data and push to backend storage.
- Engineering integrations: works with CI/CD, containerization and Kubernetes for production deployments.