Open-source platform for managing the complete machine learning lifecycle — experiment tracking, model versioning, deployment, and monitoring. Provides a centralized model registry with stage transitions (staging → production), A/B testing support, and reproducible pipelines that bridge the gap between data science experimentation and production deployment.
Features
✦Experiment tracking: log parameters, metrics, artifacts, and compare runs across model versions
✦Model Registry with stage transitions (None → Staging → Production → Archived) and approval workflows
✦Reproducible ML pipelines with Docker/Kubernetes execution — any run can be re-executed identically
✦Built-in model serving with REST API endpoints, batch inference, and A/B testing traffic splitting
✦Integration with all major ML frameworks: PyTorch, TensorFlow, scikit-learn, XGBoost, HuggingFace
✦Plugin architecture: extend with custom backends, storage, and deployment targets (SageMaker, Azure ML, Vertex AI)
Pricing
basicFree (self-hosted)
proManaged from $50/mo
enterpriseEnterprise: SSO, RBAC, unlimited models
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