ClearML is an open-source MLOps platform that helps data scientists and ML engineers automate, manage, and scale their entire machine learning workflow, from experiment tracking to production deployment.
ClearML provides an open-source, end-to-end Machine Learning Operations (MLOps) platform designed for data science teams and AI-driven organizations. It unifies the entire ML lifecycle by offering tools for experiment tracking, data versioning, pipeline orchestration, and model deployment within a single system. The platform's main value proposition stems from its open-source nature, which provides immense flexibility and prevents vendor lock-in, while still offering robust, enterprise-grade features. ClearML integrates with minimal code changes into existing ML projects, automatically capturing critical information to ensure reproducibility and collaboration. This comprehensive approach empowers teams to move from research to production more efficiently and with greater control over their machine learning assets.
Data scientists, machine learning engineers, AI researchers, and DevOps teams responsible for building, training, and deploying machine learning models.
Based on 0 reviews
150,000+ users
2016
Tel Aviv, Israel
Free
For individuals and academic use. Includes 3 seats, 1-year data retention, and 100GB of storage for metrics, artifacts, media, and packages.
Free
Pro
For professional teams. Includes everything in Free plus unlimited data retention, premium support, advanced user management, and 250GB storage per seat.
$45/seat/month
Enterprise
For large-scale deployments. Includes all Pro features plus flexible hybrid/on-prem deployment, custom storage, SSO, and a dedicated Customer Success Manager.
Custom
Open Source
Deploy on your own infrastructure for full control. Includes unlimited users, projects, data, and access to all core features. Support is community-based.
Free (self-hosted)
Choose W&B for its hyper-polished UI and best-in-class experiment tracking and visualization if those are your primary needs.
A more modular open-source library from Databricks that's great if you prefer composing your own MLOps stack from individual components.
A strong competitor focused on experiment logging and model registry with a highly-rated, user-friendly interface for collaborative research teams.
The best choice for teams deeply invested in Kubernetes who want a powerful, cloud-native toolkit for composing and scaling complex ML pipelines.
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