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:
| Feature | Description |
|---|---|
| Model versioning | Track every model version with lineage back to training data and code |
| Stage transitions | Promote models through stages: None → Staging → Production |
| Access control | Unity Catalog permissions govern who can read, write, or deploy models |
| Cross-workspace | Share models across workspaces using Unity Catalog's three-level namespace |
| Lineage | Automatic 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.