---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: e52a42461510cd05dc70087e15210f10db7b5c0d70a307e2b7a25406f6cf9bf5
  pageDirectory: concepts
  sources:
    - open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md
  confidence: 1
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - mlflow-deployment-with-cicd-and-mlops
    - MLOps and MLflow deployment with CI/CD
    - MDWCAM
  citations:
    - file: open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md
title: MLflow deployment with CI/CD and MLOps
description: Infrastructure-as-code management of MLflow experiments and models using Declarative Automation Bundles and MLOps Stacks, plus automated staged model deployment through MLflow 3 deployment jobs integrated with Databricks Workflows and Unity Catalog.
tags:
  - mlops
  - ci-cd
  - deployment
  - mlflow
timestamp: "2026-06-19T19:49:57.634Z"
---

## MLflow deployment with CI/CD and MLOps

**MLflow deployment with CI/CD and MLOps** refers to the set of integrations that Databricks‑managed MLflow provides for automating, governing, and monitoring the lifecycle of machine learning models in production. These capabilities are built on top of the open‑source MLflow API and extend it with enterprise‑grade infrastructure as code, staged deployment pipelines, and continuous monitoring.^[open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md]

### Infrastructure as Code for CI/CD

Managed MLflow on Databricks supports managing experiments, registered models, and other AI assets through infrastructure‑as‑code patterns. Practitioners can use [Declarative Automation Bundles](/concepts/declarative-automation-bundles.md) and [MLOps Stacks](/concepts/mlops-stacks.md) to version‑control and automate the provisioning of MLflow resources, enabling repeatable, auditable CI/CD workflows.^[open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md]

### Model Deployment with CI/CD

The platform offers [MLflow 3 Deployment Jobs](/concepts/mlflow-3-deployment-jobs.md) that integrate Databricks Workflows with [Unity Catalog](/concepts/unity-catalog.md) to automate staged deployment of machine learning models. These jobs allow teams to define promotion gates (e.g., from staging to production) and enforce access controls through Unity Catalog privileges, ensuring that model deployments follow the same governance patterns as the rest of the Databricks platform.^[open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md]

### Production Monitoring and Tracing for GenAI

For generative AI applications, MLflow on Databricks provides a [production monitoring service](/concepts/production-monitoring.md) that continuously evaluates a sample of production traffic using LLM judges and scorers. This service is powered by production‑scale trace ingestion that stores traces to Unity Catalog tables, enabling analysis and alerting on model quality and drift over time.^[open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md]

### Governance and Security Integration

All deployment and monitoring workflows benefit from the underlying enterprise governance layer. [Unity Catalog](/concepts/unity-catalog.md) governs models, feature tables, vector indexes, and tools centrally. Authentication for model serving can be configured using authentication passthrough and on‑behalf‑of‑user authentication, while audit logs are captured via [system tables](/concepts/mlflow-system-tables.md) for MLflow.^[open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md]

### Related Concepts

- [Declarative Automation Bundles](/concepts/declarative-automation-bundles.md)
- [MLOps Stacks](/concepts/mlops-stacks.md)
- [MLflow 3 Deployment Jobs](/concepts/mlflow-3-deployment-jobs.md)
- Databricks Workflows
- [Unity Catalog](/concepts/unity-catalog.md)
- [Production monitoring service](/concepts/production-monitoring.md)
- [Trace ingestion](/concepts/mlflow-tracing-integrations.md)
- System tables
- Feature Store integration

### Sources

- open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md

# Citations

1. [open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md](/references/open-source-vs-managed-mlflow-on-databricks-databricks-on-aws-ce848b0f.md)
