---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: c534162b590a2515f5ba458f5dc07e775687a29d3d2a062bf6823e93dd791936
  pageDirectory: concepts
  sources:
    - how-does-databricks-support-cicd-for-machine-learning-databricks-on-aws.md
  confidence: 0.95
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - cicd-for-machine-learning
    - CFML
    - R machine learning
    - Machine Learning
    - machine learning
    - machine learning lifecycle
    - cicd-for-machine-learning-databricks
    - CFML(
    - Classic Machine Learning on Databricks
    - Machine Learning on Databricks
    - Machine Learning (ML)
  citations:
    - file: how-does-databricks-support-cicd-for-machine-learning-databricks-on-aws.md
title: CI/CD for Machine Learning
description: Automated process for developing, deploying, monitoring, and maintaining ML applications, combining MLOps, DataOps, ModelOps, and DevOps principles.
tags:
  - mlops
  - machine-learning
  - ci-cd
timestamp: "2026-06-19T19:06:46.939Z"
---

# CI/CD for Machine Learning

**CI/CD for machine learning** (continuous integration and continuous delivery) refers to an automated process for developing, deploying, monitoring, and maintaining ML applications. By automating the building, testing, and deployment of code, development teams can deliver releases more frequently and reliably than manual processes. CI/CD for ML brings together techniques from MLOps, DataOps, ModelOps, and DevOps. ^[how-does-databricks-support-cicd-for-machine-learning-databricks-on-aws.md]

In machine learning applications, CI/CD is important not only for code assets, but also for data pipelines, including both input data and the results generated by the model. ^[how-does-databricks-support-cicd-for-machine-learning-databricks-on-aws.md]

## Machine Learning Elements That Need CI/CD

A key challenge of ML development is that different teams own different parts of the process and may rely on different tools and release schedules. For machine learning tasks, the following should be tracked in an automated CI/CD workflow: training data (including data quality, schema changes, and distribution changes), input data pipelines, code for training, validating, and serving the model, and model predictions and performance. ^[how-does-databricks-support-cicd-for-machine-learning-databricks-on-aws.md]

## Integrating Databricks into CI/CD Processes

Databricks provides a single, unified data and ML platform with integrated tools to improve team efficiency and ensure consistency and repeatability of data and ML pipelines. The platform incorporates components required for the ML lifecycle including tools to build "configuration as code" for reproducibility and "infrastructure as code" to automate the provisioning of cloud services. It also includes logging and alerting services to help detect and troubleshoot problems. ^[how-does-databricks-support-cicd-for-machine-learning-databricks-on-aws.md]

The Databricks approach integrates MLOps, DataOps, ModelOps, and DevOps principles into ML workflows. ^[how-does-databricks-support-cicd-for-machine-learning-databricks-on-aws.md]

## DataOps: Reliable and Secure Data

Good ML models depend on reliable data pipelines and infrastructure. With the Databricks platform, the entire data pipeline—from ingesting data to the outputs from the served model—is on a single platform and uses the same toolset, facilitating productivity, reproducibility, sharing, and troubleshooting. ^[how-does-databricks-support-cicd-for-machine-learning-databricks-on-aws.md]

Common DataOps tasks are supported by Databricks tools such as [Unity Catalog](/concepts/unity-catalog.md) for governance and [Delta Lake](/concepts/delta-lake.md) for reliable data storage.

## ModelOps: Model Development and Lifecycle

Developing a model requires a series of experiments and a way to track and compare conditions and results. Databricks includes [MLflow](/concepts/mlflow.md) for model development tracking and the [MLflow Model Registry](/concepts/mlflow-model-registry.md) to manage the model lifecycle, including staging, serving, and storing model artifacts. ^[how-does-databricks-support-cicd-for-machine-learning-databricks-on-aws.md]

After a model is released to production, practitioners should monitor not only the model's prediction performance but also input data for changes in quality or statistical characteristics that might require retraining. ^[how-does-databricks-support-cicd-for-machine-learning-databricks-on-aws.md]

## DevOps: Production and Automation

The Databricks platform supports ML models in production with the following capabilities:

- End-to-end data and model lineage, from models in production back to raw data sources on the same platform.
- Production-level model serving that automatically scales up or down.
- [Lakeflow Jobs](/concepts/lakeflow-jobs.md) to automate and schedule ML workflows.
- [Databricks Git Folders](/concepts/databricks-git-folders-for-cicd.md) for code versioning and sharing, enabling software engineering best practices.
- Databricks Bundles (declarative automation bundles) to automate creation and deployment of Databricks resources such as jobs, registered models, and serving endpoints.
- Databricks Terraform provider to automate deployment infrastructure across clouds for ML inference jobs, serving endpoints, and featurization jobs.

^[how-does-databricks-support-cicd-for-machine-learning-databricks-on-aws.md]

### Model Serving

For deploying models to production, MLflow simplifies the process with single-click deployment as a batch job for large amounts of data or as a REST endpoint on an autoscaling cluster. The integration of [Databricks Feature Store](/concepts/databricks-feature-store.md) with MLflow ensures consistency of features for training and serving; MLflow models can automatically look up features from the Feature Store, even for low-latency online serving. ^[how-does-databricks-support-cicd-for-machine-learning-databricks-on-aws.md]

The platform supports many deployment options: code and containers, batch serving, low-latency online serving, on-device or edge serving, and multi-cloud deployments (e.g., training on one cloud and deploying on another). ^[how-does-databricks-support-cicd-for-machine-learning-databricks-on-aws.md]

### Jobs

[Lakeflow Jobs](/concepts/lakeflow-jobs.md) allow automation and scheduling of any workload, from ETL to ML. Databricks also supports integrations with third-party orchestrators like Apache Airflow. ^[how-does-databricks-support-cicd-for-machine-learning-databricks-on-aws.md]

### Git Folders

Git support in the workspace enables teams to perform Git operations through the UI. Administrators and DevOps engineers can use APIs to set up automation with their favorite CI/CD tools. Databricks supports any type of Git deployment, including private networks. These techniques, together with the [Databricks REST API](/concepts/databricks-mlflow-rest-api-20.md), allow building automated deployment processes with GitHub Actions, Azure DevOps pipelines, or Jenkins jobs. ^[how-does-databricks-support-cicd-for-machine-learning-databricks-on-aws.md]

### Unity Catalog for Governance and Security

Unity Catalog lets admins set up fine-grained access control, security policies, and governance for all data and AI assets across Databricks. ^[how-does-databricks-support-cicd-for-machine-learning-databricks-on-aws.md]

## Related Concepts

- MLOps – Operational practices for ML lifecycle management
- DataOps – Operational practices for data pipelines
- ModelOps – Operational practices for model development and monitoring
- DevOps – Operational practices for production and automation
- [MLflow](/concepts/mlflow.md) – Open-source platform for ML experiment tracking and model management
- [MLflow Model Registry](/concepts/mlflow-model-registry.md) – Central repository for managing model lifecycle
- [Databricks Feature Store](/concepts/databricks-feature-store.md) – Centralized repository for sharing and serving features
- [Unity Catalog](/concepts/unity-catalog.md) – Governance and security for data and AI assets
- [Databricks Git Folders](/concepts/databricks-git-folders-for-cicd.md) – Git integration within Databricks workspace
- [Lakeflow Jobs](/concepts/lakeflow-jobs.md) – Workload automation and scheduling
- Databricks Bundles – Declarative automation for resource deployment
- Databricks Terraform provider – Infrastructure-as-code for Databricks resources

## Sources

- how-does-databricks-support-cicd-for-machine-learning-databricks-on-aws.md

# Citations

1. [how-does-databricks-support-cicd-for-machine-learning-databricks-on-aws.md](/references/how-does-databricks-support-cicd-for-machine-learning-databricks-on-aws-48551477.md)
