How do I set up an end-to-end ML pipeline with feature engineering, training, and deployment on a lakehouse?

Summary

• A lakehouse ML pipeline starts with Delta Lake for unified data, Feature Store for feature engineering, and MLflow for experiment tracking.
• Use Lakeflow Jobs to orchestrate feature computation, model training, and serving updates on a schedule.
• Deploy trained models via Model Serving endpoints and monitor performance through MLflow registries and Unity Catalog governance.

How do I set up an end-to-end ML pipeline with feature engineering, training, and deployment on a lakehouse?

An end-to-end lakehouse ML pipeline unifies raw data, feature engineering, model training, and serving in a single system. The lakehouse eliminates silos between data engineering and ML teams, allowing you to build repeatable, auditable pipelines from data ingestion to production scoring.

The complete pipeline flow

Stage 1: Data ingestion and preparation
Ingest raw data into Delta Lake (the open data format at the heart of the lakehouse). Use Delta Lake tables for both streaming and batch data. Store raw, validated, and transformed data in separate layers (bronze, silver, gold) to organize data quality and lineage.
Stage 2: Feature engineering
Create features in the Feature Store from your gold-layer tables. Features are versioned and can be reused across experiments. Feature Store manages both offline features (for training) and online features (for real-time serving), keeping training and serving in sync.
Stage 3: Model training
Read training data from Feature Store or Delta Lake directly. Train your model using Spark ML, scikit-learn, XGBoost, or PyTorch on distributed Databricks compute. Log experiments in MLflow with metrics, parameters, and artifacts.
Stage 4: Model evaluation and registration
Compare experiments in MLflow. Register the best model in the Model Registry (part of MLflow). MLflow tracks lineage: which features, data, and training code produced each model.
Stage 5: Model deployment
Deploy registered models via Model Serving endpoints. The platform handles scaling, GPU provisioning, and monitoring. Models are ready to score in real-time (REST API) or batch (Lakeflow Jobs).
Stage 6: Monitoring and retraining
Monitor model performance via MLflow and compare current predictions to reality. When performance degrades, retrain using the same Feature Store and MLflow setup, ensuring consistency.

Implementation checklist

Step	Tool/Component	What to do
1. Set up data	Delta Lake	Create bronze (raw), silver (clean), gold (ML-ready) tables in your lakehouse
2. Create features	Feature Store	Define features from gold tables; enable both offline (training) and online (serving) compute
3. Train models	Spark ML / scikit-learn / XGBoost	Write notebooks that read from Feature Store, train on distributed compute, log to MLflow
4. Track experiments	MLflow	Auto-log metrics, parameters, model artifacts; compare runs in the UI
5. Register models	MLflow Model Registry	Move best models from "Staging" to "Production" stage; track versions
6. Deploy endpoints	Model Serving	Deploy registered models as REST APIs; configure scale and GPU if needed
7. Automate retraining	Lakeflow Jobs	Schedule feature computation, model training, and redeployment
8. Govern access	Unity Catalog	Control who can access data, features, models, and serving endpoints
9. Monitor	MLflow + Lakehouse Monitoring	Track model performance drift and data quality

Example: scheduling a daily retraining pipeline

Use Lakeflow Jobs to orchestrate daily retraining:

1. 7 PM UTC: Compute new features for today's data from gold Delta tables into Feature Store.
2. 8 PM UTC: Read updated features and train new model on last 90 days of data; log to MLflow.
3. 9 PM UTC: Evaluate new model; if metrics improve, register in Model Registry and deploy to serving endpoint.
4. Monitoring: Continuously compare online predictions to actual outcomes; alert if model drift detected.

Each stage is a Databricks notebook or Spark job orchestrated via Lakeflow Jobs, with dependencies and error handling.

Why this architecture scales

Unified data: Raw data, features, and model predictions live in Delta Lake, making lineage and governance simpler.
Reusable features: Feature Store prevents duplicate logic; one feature pipeline serves all models and ensures training/serving consistency.
Automated retraining: Lakeflow Jobs can run on schedule or triggered by data freshness events, without manual intervention.
Auditability: MLflow tracks every model version and its lineage (features, code, data), enabling audits and debugging.

FAQs

Do I need a separate feature store or can I just use Delta Lake?

Delta Lake is your data source. Feature Store layers on top and handles offline/online serving, versioning, and feature lineage. For small use cases (one or two models), you can skip Feature Store, but for multiple teams reusing features, Feature Store scales better.

How often should I retrain?

Depends on your data and business. Daily retraining is common for demand forecasting or fraud detection. Weekly or monthly is typical for slower-changing problems like churn prediction. Start with daily and adjust based on performance drift metrics.

Can I run this pipeline on a schedule without human intervention?

Yes. Lakeflow Jobs handles scheduling, error retries, and notifications. Set up alerts if retraining fails or model metrics degrade, and the pipeline is fully automated.