How to Train ML Models with Azure Machine Learning

How to Train ML Models

This guide walks you through the process of training machine learning models using Azure Machine Learning. We'll cover common workflows, best practices, and essential tools.

1. Set up Your Training Environment

Before you can train a model, you need a compute resource. Azure Machine Learning offers several options:

Compute Instances: A managed cloud-based workstation for development.
Compute Clusters: Scalable clusters of VMs for distributed training.
Inference Clusters: For deploying trained models.

To create a compute resource, navigate to the 'Compute' section in your Azure Machine Learning workspace studio and select 'Compute clusters' or 'Compute instances'.

2. Prepare Your Data

High-quality data is crucial for successful model training. Azure Machine Learning provides tools for data preparation:

Datastores: Connect to your data sources (e.g., Azure Blob Storage, Azure Data Lake Storage).
Datasets: Create curated views of your data for training.
Data Transformation: Use Python SDK or visual tools to clean and transform data.

Learn more about managing and preparing data.

3. Choose a Training Method

Azure Machine Learning supports various training methods to suit different needs:

a) Automated ML (AutoML)

AutoML automates the model selection and hyperparameter tuning process. It's ideal for users who want to quickly find a good model without extensive ML expertise.

Tip: Use AutoML for baseline models or when exploring a wide range of algorithms.

You can launch AutoML from the Azure Machine Learning studio or use the Python SDK.

b) Custom Training with SDK

For more control, you can write custom training scripts using popular ML frameworks like TensorFlow, PyTorch, scikit-learn, and XGBoost.

A typical custom training script involves:

Loading your data.
Defining your model architecture.
Specifying training parameters (learning rate, epochs, etc.).
Training the model.
Logging metrics and saving the trained model.

You then submit this script as a 'Job' to your compute target in Azure Machine Learning.


from azureml.core import Workspace, Experiment, ScriptRunConfig

# Load workspace
ws = Workspace.from_config()

# Define experiment
experiment = Experiment(workspace=ws, name='train-model-experiment')

# Configure the training job
src = ScriptRunConfig(source_directory='.',
                      script='train.py',
                      compute_target='cpu-cluster') # Your compute target name

# Submit the job
run = experiment.submit(src)
run.wait_for_completion(show_output=True)

print(f"Run ID: {run.id}")

c) Designer

Azure Machine Learning designer provides a visual, drag-and-drop interface for building and training ML models without coding. It's great for rapid prototyping and for users who prefer a graphical approach.

4. Track and Log Experiments

Effective experiment tracking is essential for reproducibility and comparison.

Run Logging: Log metrics, parameters, and model outputs using the MLflow SDK or Azure ML SDK.
Experiments: Organize runs into experiments to manage different training attempts.
Model Registration: Register your trained models with their associated metrics and versions.

The Azure Machine Learning studio provides a comprehensive dashboard to view and analyze your experiment runs.

Note: Always log key metrics like accuracy, precision, recall, and loss to evaluate model performance.

5. Hyperparameter Tuning

Optimizing hyperparameters can significantly improve model performance.

Azure Machine Learning offers built-in hyperparameter tuning capabilities:

Grid Search: Exhaustively searches a predefined set of hyperparameter values.
Random Search: Randomly samples hyperparameter combinations.
Bayesian Optimization: More intelligent search that uses past results to guide future choices.

You can configure these tuning jobs directly within the studio or via the SDK.

Best Practices for Training

Start with a simple baseline model.
Use version control for your training code and data.
Monitor resource utilization during training.
Save intermediate model checkpoints for long-running jobs.
Document your experiments thoroughly.

Important: Ensure your training data is representative of the data your model will encounter in production.

Next Steps

Once you have successfully trained a model, you'll want to deploy it to make predictions.