Why it matters

MLflow is de facto tracking. Understanding shapes ML ops.

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The architecture

Tracking: log params + metrics + artifacts.

Registry: versioned models.

Deploy: to serving.

MLflow stackTrackingparams + metricsRegistryversioned modelsDeployto servingMLflow + Databricks integration common; self-host also viable
MLflow.
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How it works end to end

Auto-logging: PyTorch + HF integrations.

Metadata store: SQL backend.

Artifact store: S3/GCS/blob.

Deploy: to KServe, Sagemaker, etc.