Polyaxon is an open-source machine learning operations (MLOps) platform built to help individuals, teams, and organizations develop, train, orchestrate, and monitor machine learning and deep learning workflows at scale with reproducibility and automation as core principles. It provides a unified solution for tracking experiments, managing datasets, scheduling jobs, and comparing results across runs, which greatly improves productivity and collaboration in data science teams. Polyaxon integrates seamlessly with Kubernetes and container orchestration so that workloads can be scheduled efficiently, GPU and CPU resources are shared, and distributed training across multiple nodes is straightforward. It supports connection to external Git repositories for source-controlled experiments, making it easy to pull code directly for runs and enabling continuous integration workflows with tools like GitHub Actions.
Features
- End-to-end machine learning lifecycle orchestration and automation
- Experiment tracking with metrics, artifacts, and logs
- Kubernetes-native distributed training and job scheduling
- Pipeline orchestration with complex workflows and dependency tracking
- Integration with Git repos and CI/CD pipelines
- Model registry and versioning with metadata and lineage tracking