1. Machine Learning Model Training with Databricks MLflow


    To set up machine learning model training with Databricks using MLflow, you'll typically go through the following steps:

    1. Create an MLflow experiment to track all the runs for our model training.
    2. Define model training runs inside our experiment, which includes code to train the model using various parameters.
    3. After training, register the model in the MLflow Model Registry for versioning and manage the entire lifecycle of the model including staging, production, and archiving.

    In the Pulumi program below, we will define resources for setting up an MLflow experiment and MLflow model components which would be the skeleton for managing machine learning lifecycle on Databricks via Pulumi.

    Let's go through the necessary steps in a Pulumi Python program:

    1. First, ensure you have the Databricks provider set up within your Pulumi program.
    2. Import the necessary Databricks modules.
    3. Create an MlflowExperiment to track our model training.
    4. Set up an MlflowModel to represent our trained model.

    Below is a Pulumi program that demonstrates how to set up machine learning model training with Databricks MLflow:

    import pulumi import pulumi_databricks as databricks # Ensure you have configured the Databricks provider with the necessary authentication # and workspace details in your Pulumi setup prior to running this program. # Create an MLflow Experiment to track machine learning training runs. mlflow_experiment = databricks.MlflowExperiment("mlflow-experiment", name="pulumi-ml-experiment", description="A machine learning experiment managed by Pulumi" ) # Register an MLflow Model in the Model Registry. mlflow_model = databricks.MlflowModel("mlflow-model", name="pulumi-registered-model", description="A machine learning model trained and registered via Pulumi", tags=[ {"key": "project", "value": "Pulumi ML Demo"} ] ) # The model and experiment can be connected via an actual training script or pipeline # that uses MLflow's API to log runs and register models. This action happens outside of # Pulumi's orchestration and would be part of your model training scripts. # Export the IDs of the experiment and model to be accessible outside of Pulumi. pulumi.export("experiment_id", mlflow_experiment.experiment_id) pulumi.export("model_name", mlflow_model.name)

    In this program:

    • We create an MLflow experiment to track all the runs for our model training using the MlflowExperiment resource. The experiment is where all training runs will be logged, including parameters, metrics, and output artifacts.
    • We also set up an MLflow model using the MlflowModel resource. This is a logical container for one or more model versions. You may think of it as a repository for your ML models where each model can have multiple iterations or versions.

    Once you have these resources in place, you would use MLflow's APIs within your data science scripts to log training runs to the experiment and register the resulting models to the MLflow Model Registry. The Pulumi program simply sets up the necessary components on the Databricks platform.

    Ensure to install the pulumi_databricks Python package:

    pip install pulumi_databricks

    You can follow the MLflow Experiment documentation and MLflow Model documentation for more detailed information.