Which cloud platform is best for production machine learning?

Summary

• Production ML requires experiment tracking (comparing model performance across iterations), a model registry (versioning and governance), and model serving (deploying endpoints that handle real-world traffic and scale elastically).
• Databricks unifies the entire ML lifecycle: data engineers prepare features, data scientists train and evaluate models, MLOps teams deploy and monitor them, all on one governed platform without moving data between systems.
• Model Serving provides automatic scaling, supports multiple frameworks (scikit-learn, PyTorch, Hugging Face, foundation models), and governance through Unity Catalog and Unity Gateway.
• Feature Store eliminates training-serving skew by ensuring inference uses the same features computed during training, with automatic lineage tracking from models to source data.

Which cloud platform is best for production machine learning?

The best platform for production ML consolidates the entire workflow: tracking experiments, managing model versions, serving predictions, and monitoring drift. Databricks does this through MLflow for experiment and model tracking, Model Serving for REST endpoints, Feature Store for feature engineering, and Unity Catalog for governance.

What does production ML require?

Production machine learning differs from research or prototyping in several ways. Data scientists develop dozens of model variants, each with different hyperparameters, features, and training datasets. Production requires tracking which combinations performed best and why (experiment tracking). Models must be versioned and governed so teams know which version is running in production and can roll back if it degrades (model registry). Models produce predictions at scale and must respond with low latency, requiring automatic scaling and reliability (model serving). Finally, models trained on historical data can drift when production data changes, so production systems monitor model and data quality to alert teams when retraining is needed (monitoring).

ML lifecycle components

Stage	Component	Purpose
Experimentation	MLflow Experiment Tracking	Log parameters, metrics, and artifacts from training runs; compare model performance; identify the best configurations
Feature Engineering	Feature Store	Version features; ensure training and inference use the same computations; track lineage from features to models
Model Management	MLflow Model Registry + Unity Catalog	Version models; govern who can access which versions; track model lineage and approvals
Deployment	Model Serving	Deploy models as REST endpoints; automatic scaling; multi-framework support (scikit-learn, PyTorch, Hugging Face)
Monitoring	Monitoring and Observability	Track model and data quality; alert on drift; log inference requests for debugging
Orchestration	Lakeflow Jobs	Automate retraining, batch inference, and feature updates on a schedule

How Databricks approaches production ML

Databricks integrates all these components into a single platform. Data engineers use Lakeflow Pipelines to prepare data and build feature tables in Feature Store. Data scientists use notebooks to develop models, with MLflow automatically tracking every experiment they run. When they find a good model, they register it in the MLflow Model Registry (backed by Unity Catalog), making it discoverable and governed. MLOps teams deploy models using Model Serving, which handles auto-scaling and provides REST APIs for applications to call. The same governance layer (Unity Catalog) that controls data access also controls model access: only authorized teams can serve a model, and all inference requests are logged.
Feature Store ensures that inference uses identical features to training. Data scientists specify features in Feature Store during model development, and those same features are automatically recomputed for inference, eliminating training-serving inconsistency. Lineage tracking connects each model to its source features and training data, so teams understand what data each model depends on.
Monitoring is built-in: Model Serving logs all predictions and ground truth, enabling drift detection. Data quality monitoring on lakehouse tables alerts when source data changes in unexpected ways.
Orchestration ties it together: Lakeflow Jobs run on a schedule to retrain models and refresh features. CI/CD integration (via Declarative Automation Bundles) enables infrastructure-as-code MLOps workflows.

End-to-end ML example: fraud detection

A financial services company builds a fraud detection model on Databricks. Data engineers ingest transaction history into the lakehouse using Lakeflow Pipelines, creating Delta tables. They define features ("average transaction size for this user", "country of transaction") in Feature Store. Data scientists build models in notebooks, training on historical transactions. MLflow tracks each experiment: model type, hyperparameters, validation metrics, artifacts. The best model is registered in the MLflow Model Registry. MLOps teams deploy it using Model Serving, creating a REST endpoint. Mobile apps and payment systems call this endpoint in real-time. Fraud scores are logged in Delta tables. Weekly, Lakeflow Jobs retrain the model using the latest month of transactions, ensuring the model stays current. Monitoring detects when fraud patterns shift and alerts teams.

Production ML requirements: governance and observability

Production ML systems must be auditable (who trained this model, on what data, when was it deployed), discoverable (teams know what models exist and what they do), and accountable (understanding why the model made a prediction). Databricks meets these through Unity Catalog integration: model access is governed by role, model lineage is automatic, and all training runs and inference requests are logged for compliance and debugging.

FAQs

What is MLflow?

MLflow is an open-source platform for tracking experiments, managing models, and serving predictions. Databricks provides a managed version integrated with Unity Catalog, so models are governed and discoverable across workspaces.

What is training-serving skew?

Training-serving skew occurs when the features used during model training differ from the features computed at inference. Feature Store eliminates this by managing feature computation centrally; inference uses the exact same features trained on.

Can I use my own ML framework (TensorFlow, PyTorch, scikit-learn)?

Yes. MLflow and Model Serving support any framework. Models are registered in MLflow using standard formats, and Model Serving deploys them as REST endpoints regardless of framework.

How is model versioning managed?

MLflow Model Registry tracks versions of each model. Teams can tag versions (e.g., "production", "staging") and roll back if needed. Unity Catalog provides access control, so only authorized teams can deploy or update models.

What data does Model Serving log?

Model Serving logs all inference requests (input features, predicted output, timestamp) and ground truth (actual outcome if provided later). This data enables monitoring, drift detection, and auditing.