Databricks MLOps Stacks

NOTE: This feature is in public preview.

This repo provides a customizable stack for starting new ML projects on Databricks that follow production best-practices out of the box.

Using Databricks MLOps Stacks, data scientists can quickly get started iterating on ML code for new projects while ops engineers set up CI/CD and ML resources management, with an easy transition to production. You can also use MLOps Stacks as a building block in automation for creating new data science projects with production-grade CI/CD pre-configured. More information can be found at https://docs.databricks.com/en/dev-tools/bundles/mlops-stacks.html.

The default stack in this repo includes three modular components:

Component	Description	Why it's useful
ML Code	Example ML project structure (training and batch inference, etc), with unit tested Python modules and notebooks	Quickly iterate on ML problems, without worrying about refactoring your code into tested modules for productionization later on.
ML Resources as Code	ML pipeline resources (training and batch inference jobs, etc) defined through databricks CLI bundles	Govern, audit, and deploy changes to your ML resources (e.g. "use a larger instance type for automated model retraining") through pull requests, rather than adhoc changes made via UI.
CI/CD(GitHub Actions or Azure DevOps)	GitHub Actions or Azure DevOps workflows to test and deploy ML code and resources	Ship ML code faster and with confidence: ensure all production changes are performed through automation and that only tested code is deployed to prod

See the FAQ for questions on common use cases.

ML pipeline structure and development loops

An ML solution comprises data, code, and models. These resources need to be developed, validated (staging), and deployed (production). In this repository, we use the notion of dev, staging, and prod to represent the execution environments of each stage.

An instantiated project from MLOps Stacks contains an ML pipeline with CI/CD workflows to test and deploy automated model training and batch inference jobs across your dev, staging, and prod Databricks workspaces.

Data scientists can iterate on ML code and file pull requests (PRs). This will trigger unit tests and integration tests in an isolated staging Databricks workspace. Model training and batch inference jobs in staging will immediately update to run the latest code when a PR is merged into main. After merging a PR into main, you can cut a new release branch as part of your regularly scheduled release process to promote ML code changes to production.

Develop ML pipelines

mlops_stacks_01_ml_dev.mov

Create a PR and CI

mlops_stacks_02_create_pr.mov

Merge the PR and deploy to Staging

mlops_stacks_03_merge_PR.mov

mlops_stacks_04_deploy_to_staging.mov

Deploy to Prod

mlops_stacks_05_release.mov

See this page for detailed description and diagrams of the ML pipeline structure defined in the default stack.

Using MLOps Stacks

Prerequisites

• Python 3.8+
• Databricks CLI >= v0.221.0

Databricks CLI contains Databricks asset bundle templates for the purpose of project creation.

Please follow the instruction to install and set up databricks CLI. Releases of databricks CLI can be found in the releases section of databricks/cli repository.

Databricks asset bundles and Databricks asset bundle templates are in public preview.

Start a new project

To create a new project, run:

databricks bundle init mlops-stacks

This will prompt for parameters for initialization. Some of these parameters are required to get started:

• input_setup_cicd_and_project : If both CI/CD and the project should be set up, or only one of them.
  • CICD_and_Project - Setup both CI/CD and project, the default option.
  • Project_Only - Setup project only, easiest for Data Scientists to get started with.
  • CICD_Only - Setup CI/CD only, likely for monorepo setups or setting up CI/CD on an already initialized project. We expect Data Scientists to specify Project_Only to get started in a development capacity, and when ready to move the project to Staging/Production, CI/CD can be set up. We expect that step to be done by Machine Learning Engineers (MLEs) who can specify CICD_Only during initialization and use the provided workflow to setup CI/CD for one or more projects.
• input_root_dir: name of the root directory. When initializing with CICD_and_Project, this field will automatically be set to input_project_name.
• input_cloud: Cloud provider you use with Databricks (AWS, Azure, or GCP).

Others must be correctly specified for CI/CD to work:

• input_cicd_platform : CI/CD platform of choice. Currently we support GitHub Actions, GitHub Actions for GitHub Enterprise Servers, Azure DevOps and GitLab.
• input_databricks_staging_workspace_host: URL of staging Databricks workspace, used to preview config changes before they're deployed to production.
  We encourage granting data scientists working on the current ML project non-admin (read) access to this workspace, to enable them to view and debug CI test results
• input_databricks_prod_workspace_host: URL of production Databricks workspace. We encourage granting data scientists working on the current ML project non-admin (read) access to this workspace, to enable them to view production job status and see job logs to debug failures.
• input_default_branch: Name of the default branch, where the prod and staging ML resources are deployed from and the latest ML code is staged.
• input_release_branch: Name of the release branch. The production jobs (model training, batch inference) defined in this repo pull ML code from this branch.

Or used for project initialization:

• input_project_name: name of the current project
• input_read_user_group: User group name to give READ permissions to for project resources (ML jobs, integration test job runs, and machine learning resources). A group with this name must exist in both the staging and prod workspaces. Defaults to "users", which grants read permission to all users in the staging/prod workspaces. You can specify a custom group name e.g. to restrict read permissions to members of the team working on the current ML project.
• input_include_models_in_unity_catalog: If selected, models will be registered to Unity Catalog. Models will be registered under a three-level namespace of <catalog>.<schema_name>.<model_name>, according the the target environment in which the model registration code is executed. Thus, if model registration code runs in the prod environment, the model will be registered to the prod catalog under the namespace <prod>.<schema>.<model_name>. This assumes that the respective catalogs exist in Unity Catalog (e.g. dev, staging and prod catalogs). Target environment names, and catalogs to be used are defined in the Databricks bundles files, and can be updated as needed.
• input_schema_name: If using Models in Unity Catalog, specify the name of the schema under which the models should be registered, but we recommend keeping the name the same as the project name. We default to using the same schema_name across catalogs, thus this schema must exist in each catalog used. For example, the training pipeline when executed in the staging environment will register the model to staging.<schema_name>.<model_name>, whereas the same pipeline executed in the prod environment will register the mode to prod.<schema_name>.<model_name>. Also, be sure that the service principals in each respective environment have the right permissions to access this schema, which would be USE_CATALOG, USE_SCHEMA, MODIFY, CREATE_MODEL, and CREATE_TABLE.
• input_unity_catalog_read_user_group: If using Models in Unity Catalog, define the name of the user group to grant EXECUTE (read & use model) privileges for the registered model. Defaults to "account users".
• input_include_feature_store: If selected, will provide Databricks Feature Store stack components including: project structure and sample feature Python modules, feature engineering notebooks, ML resource configs to provision and manage Feature Store jobs, and automated integration tests covering feature engineering and training.
• input_include_mlflow_recipes: If selected, will provide MLflow Recipes stack components, dividing the training pipeline into configurable steps and profiles.

See the generated README.md for next steps!

Customize MLOps Stacks

Your organization can use the default stack as is or customize it as needed, e.g. to add/remove components or adapt individual components to fit your organization's best practices. See the stack customization guide for more details.

FAQ

Do I need separate dev/staging/prod workspaces to use MLOps Stacks?

We recommend using separate dev/staging/prod Databricks workspaces for stronger isolation between environments. For example, Databricks REST API rate limits are applied per-workspace, so if using Databricks Model Serving, using separate workspaces can help prevent high load in staging from DOSing your production model serving endpoints.

However, you can create a single workspace stack, by supplying the same workspace URL for input_databricks_staging_workspace_host and input_databricks_prod_workspace_host. If you go this route, we recommend using different service principals to manage staging vs prod resources, to ensure that CI workloads run in staging cannot interfere with production resources.

I have an existing ML project. Can I productionize it using MLOps Stacks?

Yes. Currently, you can instantiate a new project and copy relevant components into your existing project to productionize it. MLOps Stacks is modularized, so you can e.g. copy just the GitHub Actions workflows under .github or ML resource configs under {{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/resources and {{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/databricks.yml into your existing project.

Can I adopt individual components of MLOps Stacks?

For this use case, we recommend instantiating via Databricks asset bundle templates and copying the relevant subdirectories. For example, all ML resource configs are defined under {{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/resources and {{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/databricks.yml, while CI/CD is defined e.g. under .github if using GitHub Actions, or under .azure if using Azure DevOps.

Can I customize my MLOps Stack?

Yes. We provide the default stack in this repo as a production-friendly starting point for MLOps. However, in many cases you may need to customize the stack to match your organization's best practices. See the stack customization guide for details on how to do this.

Does the MLOps Stacks cover data (ETL) pipelines?

Since MLOps Stacks is based on databricks CLI bundles, it's not limited only to ML workflows and resources - it works for resources across the Databricks Lakehouse. For instance, while the existing ML code samples contain feature engineering, training, model validation, deployment and batch inference workflows, you can use it for Delta Live Tables pipelines as well.

How can I provide feedback?

Please provide feedback (bug reports, feature requests, etc) via GitHub issues.

Contributing

We welcome community contributions. For substantial changes, we ask that you first file a GitHub issue to facilitate discussion, before opening a pull request.

MLOps Stacks is implemented as a Databricks asset bundle template that generates new projects given user-supplied parameters. Parametrized project code can be found under the {{.input_root_dir}} directory.

Installing development requirements

To run tests, install actionlint, databricks CLI, npm, and act, then install the Python dependencies listed in dev-requirements.txt:

pip install -r dev-requirements.txt

Running the tests

NOTE: This section is for open-source developers contributing to the default stack in this repo. If you are working on an ML project using the stack (e.g. if you ran databricks bundle init to start a new project), see the README.md within your generated project directory for detailed instructions on how to make and test changes.

Run unit tests:

pytest tests

Run all tests (unit and slower integration tests):

pytest tests --large

Run integration tests only:

pytest tests --large-only

Previewing changes

When making changes to MLOps Stacks, it can be convenient to see how those changes affect a generated new ML project. To do this, you can create an example project from your local checkout of the repo, and inspect its contents/run tests within the project.

We provide example project configs for Azure (using both GitHub and Azure DevOps), AWS (using GitHub), and GCP (using GitHub) under tests/example-project-configs. To create an example Azure project, using Azure DevOps as the CI/CD platform, run the following from the desired parent directory of the example project:

# Note: update MLOPS_STACKS_PATH to the path to your local checkout of the MLOps Stacks repo
MLOPS_STACKS_PATH=~/mlops-stacks
databricks bundle init "$MLOPS_STACKS_PATH" --config-file "$MLOPS_STACKS_PATH/tests/example-project-configs/azure/azure-devops.json"

To create an example AWS project, using GitHub Actions for CI/CD, run:

# Note: update MLOPS_STACKS_PATH to the path to your local checkout of the MLOps Stacks repo
MLOPS_STACKS_PATH=~/mlops-stacks
databricks bundle init "$MLOPS_STACKS_PATH" --config-file "$MLOPS_STACKS_PATH/tests/example-project-configs/aws/aws-github.json"

To create an example GCP project, using GitHub Actions for CI/CD, run:

# Note: update MLOPS_STACKS_PATH to the path to your local checkout of the MLOps Stacks repo
MLOPS_STACKS_PATH=~/mlops-stacks
databricks bundle init "$MLOPS_STACKS_PATH" --config-file "$MLOPS_STACKS_PATH/tests/example-project-configs/gcp/gcp-github.json"