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MLflow: Experiment Tracking & Model Registry
Track experiments, compare runs, manage models with registry, and standardize the ML lifecycle for teams.
MLflowExperiment TrackingModel RegistryMLOps
Duration
2 days
Format
Remote
Level
Intermediate
Key outcomes
- Log params/metrics/artifacts correctly
- Compare runs and standardize experiments
- Use model registry and versioning practices
- Promote models across environments (dev → prod)
Program
Detailed syllabus will be published soon. Contact us to receive the latest version.