/mlops_sample_demo

Primary LanguagePython

mlops_sample_demo

The 'mlops_sample_demo' project was generated by using the default-python template. This template will be extended to implement an E2E complex requirements like

  • How to add external dependencies to the job cluster as policies / or use Docker Container runtimes
  • How to create a WHL file by adding the OSS project source code into runtime
  • How to build a model and link the requirements and whl file reference in the MLflow Model object

Getting started

  1. Install the Databricks CLI from https://docs.databricks.com/dev-tools/cli/databricks-cli.html

  2. Authenticate to your Databricks workspace:

    $ databricks configure

  3. To deploy a development copy of this project, type:

    $ databricks bundle deploy --target dev

    (Note that "dev" is the default target, so the --target parameter is optional here.)

    This deploys everything that's defined for this project. For example, the default template would deploy a job called [dev yourname] mlops_sample_demo_job to your workspace. You can find that job by opening your workpace and clicking on Workflows.

  4. Similarly, to deploy a production copy, type:

    $ databricks bundle deploy --target prod

  5. To run a job or pipeline, use the "run" command:

    $ databricks bundle run

  6. Optionally, install developer tools such as the Databricks extension for Visual Studio Code from https://docs.databricks.com/dev-tools/vscode-ext.html. Or read the "getting started" documentation for Databricks Connect for instructions on running the included Python code from a different IDE.

  7. For documentation on the Databricks asset bundles format used for this project, and for CI/CD configuration, see https://docs.databricks.com/dev-tools/bundles/index.html.