Book Image

Azure Machine Learning Engineering

By : Sina Fakhraee, Balamurugan Balakreshnan, Megan Masanz
Book Image

Azure Machine Learning Engineering

By: Sina Fakhraee, Balamurugan Balakreshnan, Megan Masanz

Overview of this book

Data scientists working on productionizing machine learning (ML) workloads face a breadth of challenges at every step owing to the countless factors involved in getting ML models deployed and running. This book offers solutions to common issues, detailed explanations of essential concepts, and step-by-step instructions to productionize ML workloads using the Azure Machine Learning service. You’ll see how data scientists and ML engineers working with Microsoft Azure can train and deploy ML models at scale by putting their knowledge to work with this practical guide. Throughout the book, you’ll learn how to train, register, and productionize ML models by making use of the power of the Azure Machine Learning service. You’ll get to grips with scoring models in real time and batch, explaining models to earn business trust, mitigating model bias, and developing solutions using an MLOps framework. By the end of this Azure Machine Learning book, you’ll be ready to build and deploy end-to-end ML solutions into a production system using the Azure Machine Learning service for real-time scenarios.

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Table of Contents (17 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
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1
Part 1: Training and Tuning Models with the Azure Machine Learning Service
Part 1: Training and Tuning Models with the Azure Machine Learning Service
Free Chapter
2
Chapter 1: Introducing the Azure Machine Learning Service
Chapter 1: Introducing the Azure Machine Learning Service
Technical requirements
Building your first AMLS workspace
Navigating AMLS
Creating a compute for writing code
Developing within AMLS
Connecting AMLS to VS Code
Summary
3
Chapter 2: Working with Data in AMLS
Chapter 2: Working with Data in AMLS
Technical requirements
Azure Machine Learning datastore overview
Creating a blob storage account datastore
Creating Azure Machine Learning data assets
Creating a data asset using the Python SDK
Using Azure Machine Learning datasets
Summary
4
Chapter 3: Training Machine Learning Models in AMLS
Chapter 3: Training Machine Learning Models in AMLS
Technical requirements
Training code-free models with the designer
Training on a compute instance
Training on a compute cluster
Summary
5
Chapter 4: Tuning Your Models with AMLS
Chapter 4: Tuning Your Models with AMLS
Technical requirements
Understanding model parameters
Sampling hyperparameters
Understanding sweep jobs
Setting up a sweep job with grid sampling
Setting up a sweep job for random sampling
Setting up a sweep job for Bayesian sampling
Reviewing results of a sweep job
Summary
6
Chapter 5: Azure Automated Machine Learning
Chapter 5: Azure Automated Machine Learning
Technical requirements
Introduction to Azure AutoML
Featurization concepts in AML
AutoML using AMLS
AutoML using the AML Python SDK
Parsing your AutoML results via AMLS and the AML SDK
Summary
7
Part 2: Deploying and Explaining Models in AMLS
Part 2: Deploying and Explaining Models in AMLS
8
Chapter 6: Deploying ML Models for Real-Time Inferencing
Chapter 6: Deploying ML Models for Real-Time Inferencing
Technical requirements
Understanding real-time inferencing and batch scoring
Deploying an MLflow model with managed online endpoints through AML Studio
Deploying an MLflow model with managed online endpoints through the Python SDK V2
Deploying a model with managed online endpoints through the Python SDK v2
Deploying a model for real-time inferencing with managed online endpoints through the Azure CLI v2
Summary
9
Chapter 7: Deploying ML Models for Batch Scoring
Chapter 7: Deploying ML Models for Batch Scoring
Technical requirements
Deploying a model for batch inferencing using the Studio
Deploying a model for batch inferencing through the Python SDK
Summary
10
Chapter 8: Responsible AI
Chapter 8: Responsible AI
Responsible AI principles
Responsible AI Toolbox overview
Responsible AI dashboard
Error analysis dashboard
Interpretability dashboard
Fairness
Summary
11
Chapter 9: Productionizing Your Workload with MLOps
Chapter 9: Productionizing Your Workload with MLOps
Technical requirements
Understanding the MLOps implementation
Preparing your MLOps environment
Running an Azure DevOps pipeline
Summary
Further reading
12
Part 3: Productionizing Your Workload with MLOps
Part 3: Productionizing Your Workload with MLOps
13
Chapter 10: Using Deep Learning in Azure Machine Learning
Chapter 10: Using Deep Learning in Azure Machine Learning
Technical requirements
Labeling image data using the Data Labeling feature of Azure Machine Learning
Training an object detection model using Azure AutoML
Deploying the object detection model to an online endpoint using the Azure ML Python SDK
Summary
14
Chapter 11: Using Distributed Training in AMLS
Chapter 11: Using Distributed Training in AMLS
Technical requirements
Data parallelism
Model parallelism
Distributed training with PyTorch
Distributed training code
Distributed training with TensorFlow
Summary
15
Index
Index
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16
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Introducing the Azure Machine Learning Service

Machine Learning (ML), leveraging data to build and train a model to make predictions, is rapidly maturing. Azure Machine Learning (AML) is Microsoft’s cloud service, which not only enables model development but also your data science life cycle. AML is a tool designed to empower data scientists, ML engineers, and citizen data scientists. It provides a framework to train and deploy models empowered through MLOps to monitor, retrain, evaluate, and redeploy models in a collaborative environment backed by years of feedback from Microsoft’s Fortune 500 customers.

In this chapter, we will focus on deploying an AML workspace, the resource that leverages Azure resources to provide an environment to bring together the assets you will leverage when you use AML. We will showcase how to deploy these resources using a Guided User Interface (GUI), followed by setting up your AML service via the Azure Command-Line Interface (CLI) ml extension (v2), which is the ml extension for the Azure CLI, allowing model training and deployment through the command line. We will proceed with setting up the workspace by leveraging Azure Resource Management (ARM) templates, which are referred to as ARM deployments.

During deployment, key resources will be deployed, including AML Studio, a portal for data scientists to manage their workload, often referred to as your workspace; Azure Key Vault for storing sensitive information; Application Insights for logging information; Azure Container Registry to store docker images to leverage; and an Azure storage account to hold data. These resources will be leveraged behind the scenes as you navigate through the Azure Machine Learning service workspace, creating compute resources for writing code by leveraging the Integrated Development Environments (IDE) of your choice, including Jupyter Notebook, Jupyter Lab, as well as VS Code.

In this chapter, we will cover the following topics:

  • Building your first AMLS workspace
  • Navigating AMLS
  • Creating a compute for writing code
  • Developing within AMLS
  • Connecting AMLS to VS Code
Previous Section
End of Section 1
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