Automating Machine Learning with Azure ML and Azure DevOps

Azure Machine Learning (ML) enables seamless model building, training, and deployment using powerful cloud-based resources. By integrating Azure DevOps and leveraging a Linux Virtual Machine (VM), we can automate the process from model training to deployment. In this article, we will walk through the steps of creating an Azure ML pipeline that builds, trains, evaluates, and deploys a model with Azure DevOps.

Overview of the Workflow

Create or Get a Namespace (Azure ML Workspace)
Train the Model on Linux VM and Register it in Azure ML Workspace
Evaluate the Trained Model Against the Production Model
Register the Trained Model in Azure ML
Create a Docker Image for Model Deployment

Let’s go step-by-step to understand each process in detail.

1. Create or Get a Namespace (Azure ML Workspace)

The first step is to either create a new Azure ML workspace or get an existing one. This workspace acts as a central repository for all your machine learning experiments, datasets, models, and resources.

Internally, when you create the Azure ML workspace, Azure provisions the following resources:

Azure Key Vault: Securely manages secrets, credentials, and API keys.
Azure Storage: Stores datasets, model output, and logs.
Application Insights: Monitors the running machine learning models for performance metrics.

Additionally, Azure ML Studio is set up as part of the workspace, providing a user-friendly interface to manage machine learning models and pipelines. Underneath, this creates an isolated containerized environment for you to work with.

2. Train the Model on Linux VM and Register it in Azure ML Workspace

Once the workspace is set up, the next step is to train the model. We are utilizing a Linux VM as the host agent, with all necessary dependencies (SDKs, CLI tools, TensorFlow, etc.) installed. Azure DevOps will orchestrate this process.

Here’s how it works:

A Python or R script containing the model code is uploaded to the codebase, with a defined entry point for model training.
Azure DevOps pipelines are configured to execute this script on the Linux VM. This training process happens in the local environment.
To track and monitor progress, the Linux VM registers this model as an experiment in Azure ML Workspace using a REST API call. This API logs the parameters, model metadata, and output during training.

The VM, running locally, requires registration in the Azure ML workspace with specific parameters (like model version, experiment ID, etc.), ensuring that it is visible and managed through Azure ML Studio.

3. Evaluate the Trained Model Against the Production Model

After training, the newly generated model needs to be evaluated against the existing production model.

This evaluation step is essential to verify whether the newly trained model performs better than the currently deployed model. The evaluation criteria include metrics like accuracy, precision, recall, or any other metric relevant to the specific use case.

If the new model surpasses the production model in terms of performance metrics, the Azure DevOps pipeline can be configured to approve its deployment.

4. Register the Trained Model in Azure ML

Once the model is evaluated, and if the metrics are favorable, the next step is to register the trained model in Azure ML. Model registration helps with versioning, making it easier to manage multiple iterations of the model.

When registered, the model is tagged with metadata, such as:

Model Name and Version
Model Metrics
Environment Details (e.g., Python, libraries used)
Training Data and Parameters

This registration ensures that your model is ready for deployment or further testing at any time.

5. Create a Docker Image for Model Deployment

The final step is to package the trained and registered model into a Docker image. Docker containerization provides a standardized environment for model inference, ensuring that the model can be deployed consistently across different environments.

Azure ML can automatically create this Docker image for you, bundling the model and all its dependencies. Once the image is created, you can deploy it to any environment, such as:

Azure Kubernetes Service (AKS)
Azure Container Instances (ACI)
Any on-premises or cloud-based infrastructure that supports Docker

The Docker image acts as a scoring image for real-time or batch predictions, ensuring that your model is ready for production use.

Integrating with Azure DevOps

In our workflow, Azure DevOps plays a vital role in automating this entire pipeline. With Azure Pipelines, you can define CI/CD workflows that:

Automatically trigger the training pipeline on code changes.
Evaluate the model’s performance and determine whether to proceed with deployment.
Build and deploy the Docker image to the desired environment.

Here’s a brief example of what an Azure DevOps pipeline might look like:

yamlCopy codetrigger:
  branches:
    include:
      - main

pool:
  vmImage: 'ubuntu-latest'

steps:
- task: UsePythonVersion@0
  inputs:
    versionSpec: '3.x'

- script: |
    pip install -r requirements.txt
    python train_model.py
  displayName: 'Train Model'

- task: AzureCLI@2
  inputs:
    azureSubscription: 'AzureServiceConnection'
    scriptType: 'bash'
    scriptLocation: 'inlineScript'
    inlineScript: |
      az ml model register --model-name mymodel --model-path ./outputs/model.pkl --workspace-name myworkspace
  displayName: 'Register Model in Azure ML'

Conclusion

By integrating Azure Machine Learning, Azure DevOps, and Docker, we create a streamlined pipeline for model training, evaluation, and deployment. This approach offers flexibility and scalability, allowing data scientists to focus on improving models while leveraging cloud infrastructure for deployment.

Build CI/CD Pipelines for ML Projects with Azure Devops