ClearML is an open-source and enterprise-ready platform designed for experiment tracking, orchestration, model management, and data versioning in machine learning workflows. It enables data scientists, ML engineers, and research teams to efficiently manage their entire development lifecycle—from prototype experiments to automated pipelines.

The platform supports real-time logging, resource allocation, and reproducibility, making it suitable for both research environments and production-grade ML systems. ClearML’s modular structure allows teams to use it as a lightweight experiment tracker or as a full MLOps stack, depending on their needs.

Key benefits:

• Unified platform for tracking, scheduling, and model lifecycle management

• Designed for collaboration, scalability, and auditability

• Integrates easily with Python workflows and major ML frameworks

What are the main features of ClearML?

Experiment tracking with live logging

ClearML tracks all aspects of machine learning experiments:

• Logs hyperparameters, metrics, resource usage, and code versions

• Captures stdout, stderr, GPU utilization, and other live signals

• Automatically snapshots the code environment and configuration

• Enables filtering, searching, and comparing experiments from a web UI

Task and pipeline orchestration

Automates model training, evaluation, and deployment workflows:

• Define tasks and build pipelines via Python scripts or UI

• Schedule jobs across on-premise or cloud compute resources

• Supports autoscaling with dynamic resource allocation

• Enables reproducible, modular pipelines with version control

Model registry and deployment management

Centralized registry to manage the entire model lifecycle:

• Store, tag, and version trained models and artifacts

• Track lineage from model to training data, code, and configuration

• Integrate model serving into workflows or external systems

• Visual traceability for compliance and auditing

Data management and versioning

Supports reproducibility by handling datasets and data access:

• Register datasets and versions used in each experiment

• Tracks data provenance and dependency relationships

• Offers data deduplication and cache management

• Integrates with local and remote storage systems

Collaboration and enterprise features

Built for team-based workflows in regulated environments:

• Shared projects, user roles, and access controls

• REST API and SDKs for automation and integration

• Activity logs, tagging, and annotations for traceability

• Available as a managed service or self-hosted deployment

Why choose ClearML?

• Complete lifecycle management: from experiment tracking to deployment

• Flexible modularity: use only the components you need

• Reproducibility by default: all artifacts, code, and data are versioned

• Python-native: easy to integrate with existing ML workflows

• Scalable and enterprise-ready: for both research and production use