Experiment tracking and model versioning

For your use case, I’d focus on platforms that combine experiment tracking, model versioning, reproducibility, and a clear path from training to production. The key is to track not just which model performed best, but exactly how it was trained, which data and code produced it, and which version is actually deployed.

My shortlist is MLflow, Weights & Biases (W&B), and Comet. They cover the core workflow, but differ in how much infrastructure you manage and how they support team collaboration and deployment.mlflow.org+2

Top recommendations

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1. MLflow