MLflow is a powerful tool that helps you manage your machine learning (ML) projects.

In Microsoft Fabric, MLflow makes it easier to train, track, and use your models to make predictions on new data.

What is MLflow?

MLflow is an open-source platform to manage the ML lifecycle, including:

Tracking experiments

Logging model parameters and metrics

Saving and versioning models

Reusing models for predictions

Using MLflow in Microsoft Fabric helps you organize and reproduce your work easily.

Steps to Use MLflow in Microsoft Fabric:

1. Create an Experiment

Start by creating an experiment. Every time you train a model, it will be saved as a run under that experiment. This helps keep track of each version of your model.

2. Log Parameters and Metrics

During training, use MLflow to log:

Model parameters (like learning rate or depth)

Metrics (like accuracy or RMSE)

This helps you compare different models later.

3. Save the Model
After training, save the model in Microsoft Fabric. MLflow stores it along with:

The model file (like a .pkl file)

A metadata file called MLmodel

The environment settings to run the model

What is the MLmodel File?
The MLmodel file includes:

Path to the model (where it’s saved)

Flavors (which ML library was used, like scikit-learn)

Signature (what kind of input the model expects and what output it gives)

Customizing Model Behavior

Sometimes your model may need to be adjusted to work with new data. You can customize the input and output schema using MLflow:

Define input columns (e.g., age, gender, BMI)

Define output (e.g., prediction result)

This is important when applying the model to different datasets.

Using the Model for Batch Predictions

After saving the model, you can use it to make batch predictions in Microsoft Fabric:

1. Prepare the New Data
Make sure your data is in the correct format. The column names and types should match what the model expects.

2. Store Data in Delta Tables
Microsoft Fabric uses Delta Tables to store data in the lakehouse. To save or load data:

# Save data
df.write.format("delta").save("Tables/new_table")

# Read data
df = spark.read.format("delta").load("Tables/new_table")

3. Generate Predictions
Once your data is ready, apply the saved model to make predictions. Then, save the results for further use, like showing them in Power BI.

Important: Match Data Types
Make sure the data types in your new dataset match the model’s input schema:

Use String for text

Use Integer or Float for numbers

Use Datetime for dates and times

If the types don’t match, the model will not work correctly.

Conclusion:

MLflow in Microsoft Fabric helps you manage your machine learning process from start to finish. It makes it easy to:

Track your training process

Save and reuse models

Apply models to new data

Store and share predictions

This helps you build better models and make better decisions using your data.
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