Intermediate

MLflow on Databricks

Master experiment tracking, model registry, and model serving using Databricks' fully managed MLflow integration for end-to-end ML lifecycle management.

Managed MLflow on Databricks

MLflow is an open-source platform for managing the ML lifecycle, created by Databricks. On the Databricks platform, MLflow is fully managed with zero setup — experiment tracking, the model registry, and model serving are pre-configured and integrated with Unity Catalog.

💡
Good to know: MLflow on Databricks automatically logs experiments from notebook runs, provides a visual UI for comparing models, and integrates with Unity Catalog for governed model management across workspaces.

Experiment Tracking

Track every training run with parameters, metrics, and artifacts:

MLflow Experiment Tracking
import mlflow
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# Auto-logging captures parameters, metrics, and model
mlflow.autolog()

with mlflow.start_run(run_name="rf_experiment"):
    # Train model
    model = RandomForestClassifier(n_estimators=100, max_depth=10)
    model.fit(X_train, y_train)

    # Log custom metrics
    predictions = model.predict(X_test)
    accuracy = accuracy_score(y_test, predictions)
    mlflow.log_metric("test_accuracy", accuracy)

    # Log artifacts (plots, data samples, etc.)
    mlflow.log_artifact("feature_importance.png")

Model Registry with Unity Catalog

The Unity Catalog model registry provides governed model management:

FeatureDescription
Model versioningTrack every model version with lineage back to training data and code
Stage transitionsPromote models through stages: None → Staging → Production
Access controlUnity Catalog permissions govern who can read, write, or deploy models
Cross-workspaceShare models across workspaces using Unity Catalog's three-level namespace
LineageAutomatic lineage from model to training run, datasets, and notebooks

Model Serving

Deploy models as production REST API endpoints directly from the registry:

  • Serverless serving: Auto-scaling endpoints with pay-per-request pricing and zero infrastructure management
  • GPU serving: Serve large models and LLMs on GPU instances with optimized inference
  • A/B testing: Route traffic between model versions for safe production rollouts
  • Feature serving: Low-latency feature lookup integrated with Feature Store
  • Monitoring: Built-in payload logging, drift detection, and performance dashboards

Feature Store

Databricks Feature Store provides centralized feature management:

  • Define features as Delta tables governed by Unity Catalog
  • Automatic feature lookup at training and inference time
  • Point-in-time lookups to prevent data leakage in time-series problems
  • Online feature serving with low-latency lookups for real-time models
✅
Key takeaway: MLflow on Databricks provides a complete ML lifecycle platform. Combined with Unity Catalog governance, it enables enterprises to track experiments, manage model versions, deploy to production, and maintain full auditability of their ML assets.