Integrating ModelKits into MLflow Workflows: A Practical Example

Before You Begin

1. If you haven't aready done so, sign up for a free account with Jozu.ml

2. After you log into Jozu, add a new Repository named "white-wine-quality-predictor", which we'll use in this demo.

3. In the Project's root directory, create a .env file.

4. Edit the .env file and add an entry for your JOZU_USERNAME, your JOZU_PASSWORD and your JOZU_NAMESPACE (aka your Personal Organization name). For example:

    [email protected]
    JOZU_PASSWORD=my_password
    JOZU_NAMESPACE=brett

5. Be sure to save the changes to your .env file before continuing.

Project Setup

Set Up Your Python Environment

• This project was created using Python 3.12, but should work for Python versions >= 3.10.

• We recommend using a Python or Conda virtual environment to isolate this project's code to prevent it from affecting the system-installed Python.

• If you name your Python or Conda environment something other than ".venv" or "venv", then be sure to add the name to the .gitignore file. This step assumes you'll be using git for version control of this project.

Project Deliverables

The demo code trains and validates a model designed to predict the quality of white wine based on various features. It then creates a ModelKit from the project artifacts and pushes it to the Jozu Hub repository: "white-wine-quality-predictor".

You can view the details for these tagged ModelKit versions by viewing your white-wine-quality-predictor repository in Jozu Hub.