Azure ML Pipeline: A Step-by-Step Guide

Creating an Azure ML pipeline is a straightforward process that can be broken down into several key steps.

You'll start by defining your pipeline, which involves specifying the inputs and outputs, as well as the tasks that need to be performed.

This can include data ingestion, feature engineering, model training, and model deployment.

One of the most important aspects of defining your pipeline is selecting the right data source, which can be a database, a file system, or even a cloud storage service like Azure Blob Storage.

By following these steps, you can create a robust and scalable Azure ML pipeline that meets your needs.

Building the pipeline is a crucial step in creating an Azure ML pipeline. You can create a pipeline using either a programmatic or yaml definition.

To create a pipeline, you need to define the components that make up the workflow. A component is a reusable ML workflow that consists of several components. The typical life of a component is to write the yaml specification, register it with a name and version, load it from the pipeline code, implement the pipeline using the component's inputs, outputs, and parameters, and submit the pipeline.

A pipeline can be created using two methods: programmatic and yaml definition. You can either create the components trying both options or pick your preferred method. For simplicity, we are using the same compute for all components, but you can set different computes for each component.

To get started with building your pipeline, you need a handle to your Azure Machine Learning workspace. This handle is called ml_client and it will serve as your entry point to manage resources and jobs.

You'll create ml_client by entering your Subscription ID, Resource Group name, and Workspace name. You can find these values in the Azure Machine Learning studio toolbar, where you'll select your workspace name and copy the values.

In the Azure CLI, you'll use these values to provision the workspace and necessary compute. You'll also use the Python SDK to run a command job.

To verify the connection, you'll make a call to ml_client. This is the first time you're making a call to the workspace, so you may be asked to authenticate.

Here's a step-by-step guide to creating ml_client:

Creating ml_client will not connect to the workspace immediately. The client initialization is lazy, meaning it will wait for the first time it needs to make a call. This will happen in the next code cell, where you'll use ml_client to manage resources and jobs.

You can build the training pipeline using Azure Machine Learning pipelines, which are reusable ML workflows that consist of several components.

To build the pipeline, you'll need to write the yaml specification of each component, or create it programmatically using ComponentMethod.

There are two ways to create a component: programmatically and using yaml definition. The yaml definition can be checked-in along the code, providing a readable history tracking.

Each component has a typical life cycle: write the yaml specification, register the component with a name and version, load the component from the pipeline code, implement the pipeline using the component's inputs, outputs, and parameters, and submit the pipeline.

MLFlow is used to log the parameters and metrics during the pipeline run.

Here are the steps to create a component using yaml definition:

1. Create the yaml file describing the component.

2. Create and register the component.

3. Registering the component allows you to re-use it in other pipelines and share it with others.

You can also create a component programmatically using the CommandComponent class.

Here are the steps to create a component programmatically:

1. Create a source folder for the component.

2. Write a script that performs the desired task.

3. Create an Azure Machine Learning Component from the script.

4. Optionally, register the component in the workspace for future reuse.

The registration of a component in your Azure Machine Learning workspace is a critical step in the development of effective data pipelines.

Here are the steps to register a component:

1. Access your workspace and determine the purpose of your component.

2. Write a Python script that specifies the logic of your component.

3. Wrap the script with the AzureML Python SDK's 'ScriptRunConfig' or 'PythonScriptStep' to make it a component.

4. Register the component in your workspace by using Component.register() or PipelineStep.register().

5. Give it a name and a description for future reference.

6. Version control your components for easier monitoring and replication.

7. Use your registered component in your machine learning pipelines to optimize your data processing and model-building procedures.

Using Azure ML pipeline can significantly reduce the time it takes to train large models. It can automatically calculate which steps result are unchanged and reuse outputs from previous training.

This means that data scientists can test different training code or hyperparameters and run the training many times to get the best model performance without having to repeat the entire training process from scratch.

By properly choosing which step to run on which type of machines, the training cost can be significantly reduced. This is especially true for tasks like natural language model training, which requires pre-processing large amounts of data and GPU intensive transformer model training.

Training a model can take hours to days, but with Azure ML pipeline, you can run each step on different computation resources, such as high memory CPU machines for memory-heavy data processing work and expensive GPU machines for computation-intensive training.

Data scientists should work in branches pulled off of a master branch to avoid conflicts and ensure version control.

To ensure data quality, it's essential to clean and prepare your data properly, as poor data quality can lead to unreliable model outcomes.

Use version control to manage pipelines and models, and avoid skipping this crucial step.

Azure Machine Learning provides a range of features to support MLOps, including reusable ML pipelines, model portability, and automated deployment.

To avoid common pitfalls, keep an eye on Azure costs and ensure you're not leaving unused resources or inefficient computing.

Here are some key MLOps best practices to keep in mind:

By following these best practices, you can ensure a smooth and efficient MLOps workflow, and avoid common pitfalls that can derail your projects.

CI/CD and Automation is a crucial part of any machine learning pipeline, and Azure ML makes it easy to implement. Most projects fail before they get to production, but with an MLOps lifecycle, you can better monitor, train, and deploy your machine learning models to increase output and iteration.

To get started with CI/CD in Azure ML, you'll need to create an Azure Machine Learning workspace, which can be thought of as a container for Azure ML assets. You can create a new workspace by heading over to the creation page in the Azure Portal.

A CI/CD pipeline in Azure DevOps can be set up in just a few steps. First, you'll need to connect Azure DevOps to your Azure subscription, specifically to your Machine Learning workspace's resource group. You can do this by creating a new Service Connection of type Azure Resource Manager.

Building E2E with CI/CD is all about automating the entire process of machine learning model development, from training to deployment. This can be achieved using Azure Machine Learning (Azure ML) and Azure DevOps.

To get started, you'll need to create an Azure Machine Learning workspace, which serves as a container for Azure ML assets like compute, storage, data, and models.

Azure ML workspaces can be created in the Azure Portal, where you can choose a region and workspace edition that suits your needs.

In Azure DevOps, you'll need to connect your Azure subscription to your Machine Learning workspace's resource group.

To do this, go to Project Settings and select Service Connections, then create a new Service Connection of type Azure Resource Manager.

A YAML pipeline in Azure DevOps can be imported from a GitHub repository, which allows you to automate the entire process of machine learning model development.

Here's a step-by-step guide to importing a YAML pipeline:

Once you've imported the YAML pipeline, review the configuration to ensure everything is set up correctly, including variables and service connections.

By following these steps, you can automate the entire process of machine learning model development and deployment using Azure ML and Azure DevOps.

The next step is to submit the job to run in Azure Machine Learning. You use the create_or_update method on ml_client.jobs to do this.

You'll also need to pass an experiment name, which is a container for all the iterations you do on a certain project. This will help you track all the jobs submitted under the same experiment name in Azure Machine Learning studio.

To view the progress of your pipeline, you can use the link generated in the previous cell. Once the pipeline is complete, you can examine each component's results.

Here are the important results you'll want to see about training:

MLFlow is used to log the parameters and metrics during your pipeline run. This will help you keep track of everything and make it easier to analyze your results later.

Now that you've learned the basics of Azure Machine Learning pipelines, it's time to put them into action.

You can define pipelines using the Azure Machine Learning CLI v2, which offers a powerful way to create and manage machine learning workflows.

To get started, try out the CLI v2 pipeline example to see how it works.

If you're more comfortable with coding, you can also define pipelines using the Azure Machine Learning SDK v2.

Another option is to use the Designer, which provides a visual interface for creating and managing pipelines.

Here are some ways to create and run machine learning pipelines: