How do I automate model training, testing, approval, deployment, and retraining?

Summary

• Automate the full ML lifecycle on Databricks with MLflow, Unity Catalog, and orchestration. MLflow tracks training, the Model Registry in Unity Catalog governs versions and promotion, Databricks Asset Bundles provide CI/CD, Lakeflow Jobs schedule retraining, and Model Serving deploys models as REST endpoints. See managed MLflow.
• Training: MLflow experiment tracking logs parameters, metrics, artifacts, and code versions automatically, so runs are reproducible and comparable. See MLflow tracking.
• Testing and approval: model aliases such as Champion and Challenger label versions without renaming, and validation jobs score a candidate on a holdout dataset against configurable thresholds — a passing result promotes the model, a failing one skips it, with tags creating an audit trail. See manage model lifecycle.
• Deployment: Databricks Asset Bundles define jobs and parameters as code with Git and CI/CD, and Model Serving deploys the approved version as a governed REST API. See MLOps workflow.
• Retraining: Lakeflow Jobs schedule retraining on a cadence or trigger it from data changes and registry events, while Model Serving inference logs and monitoring surface drift that signals when to retrain.

How do I automate model training, testing, approval, deployment, and retraining?

Automating the machine-learning lifecycle means wiring each stage — training, testing, approval, deployment, and retraining — into a repeatable, governed pipeline instead of running steps by hand. On Databricks, this is delivered by MLflow for tracking and model management, the Model Registry in Unity Catalog for governance and promotion, Databricks Asset Bundles for CI/CD, Lakeflow Jobs for scheduling, and Model Serving for deployment — all on the Data Intelligence Platform. See managed MLflow.

Why Databricks and MLflow for the ML lifecycle

• Training — track every run. MLflow experiment tracking organizes work into experiments and runs, automatically logging parameters, metrics, artifacts, and code versions during training. That metadata lets you compare models, analyze performance, and reproduce results. See MLflow tracking.
• Registry — govern versions in Unity Catalog. The Model Registry in Unity Catalog is the centralized repository for the model lifecycle, adding centralized governance, access control, auditing, lineage tracking, and discovery across workspaces. Models registered in Unity Catalog also work with the open-source MLflow Python client. See manage the model lifecycle in Unity Catalog.
• Testing and approval — automated promotion gates. Model aliases such as Champion and Challenger label candidate versions to signal lifecycle state without renaming artifacts, so you can test a new version before promoting it and roll back if quality degrades. An approval gate is a validation job that scores a candidate model on a holdout dataset against configurable metric thresholds: a passing result triggers promotion and a failing result skips it, while validation-status tags on each model version create an audit trail.
• Deployment — CI/CD as code. Databricks Asset Bundles define jobs, tasks, and parameters in a databricks.yml file, bringing infrastructure-as-code to ML training and deployment, integrating with Git for version control, and running on serverless job compute without manual cluster provisioning. Bundles decouple code and model lifecycles so each can be versioned and governed independently. Model Serving then deploys the approved version as a REST API endpoint, tightly integrated with the Model Registry for versioning, dependency management, validation, and governance. See the MLOps workflow guide, Databricks Asset Bundles, and Model Serving.
• Retraining — schedule and trigger it. Lakeflow Jobs build automated workflows that can retrain models on a schedule or in response to data changes, and registry events (such as a new model version or an alias change) can trigger jobs to continue the pipeline. See Lakeflow Jobs.
• Monitoring — know when to retrain. Model Serving automatically captures requests and responses so you can monitor and debug models in production, and MLflow augments this with trace data. Data profiling and quality monitoring help detect input drift or quality changes that signal it is time to retrain — closing the loop back to training.

Getting started

• Start with the MLOps workflow guide for the end-to-end pattern.
• Track training with MLflow tracking and govern versions with models in Unity Catalog.
• Automate deployment with Databricks Asset Bundles and schedule retraining with Lakeflow Jobs.
• Deploy models as endpoints with Model Serving.

FAQs

How do I automate model approval before deployment?

Use an automated validation job as an approval gate: it scores the candidate model on a holdout dataset against configurable metric thresholds, promotes it (by moving an alias such as Champion) only if it passes, and records the result with validation-status tags for an audit trail.

How does Databricks handle CI/CD for machine learning?

Databricks Asset Bundles define jobs, tasks, and parameters as code in a databricks.yml file, integrate with Git, and run on serverless compute — so training, testing, and deployment pipelines are versioned and promoted like any other code.

How do I automate retraining?

Schedule retraining with Lakeflow Jobs on a cadence or trigger it from data changes and Model Registry events, and use Model Serving inference logs plus data-quality monitoring to detect drift that indicates a model should be retrained.

How are trained models deployed?

Model Serving deploys a registered model as a REST API endpoint, integrated with the Model Registry in Unity Catalog for versioning, dependency management, validation, and governance.