Book Image

Automated Machine Learning with Microsoft Azure

By : Dennis Michael Sawyers
Book Image

Automated Machine Learning with Microsoft Azure

By: Dennis Michael Sawyers

Overview of this book

Automated Machine Learning with Microsoft Azure will teach you how to build high-performing, accurate machine learning models in record time. It will equip you with the knowledge and skills to easily harness the power of artificial intelligence and increase the productivity and profitability of your business. Guided user interfaces (GUIs) enable both novices and seasoned data scientists to easily train and deploy machine learning solutions to production. Using a careful, step-by-step approach, this book will teach you how to use Azure AutoML with a GUI as well as the AzureML Python software development kit (SDK). First, you'll learn how to prepare data, train models, and register them to your Azure Machine Learning workspace. You'll then discover how to take those models and use them to create both automated batch solutions using machine learning pipelines and real-time scoring solutions using Azure Kubernetes Service (AKS). Finally, you will be able to use AutoML on your own data to not only train regression, classification, and forecasting models but also use them to solve a wide variety of business problems. By the end of this Azure book, you'll be able to show your business partners exactly how your ML models are making predictions through automatically generated charts and graphs, earning their trust and respect.

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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
Reviews
1
Section 1: AutoML Explained – Why, What, and How
Section 1: AutoML Explained – Why, What, and How
Free Chapter
2
Chapter 1: Introducing AutoML
Chapter 1: Introducing AutoML
Explaining data science's ROI problem
Analyzing why AI projects fail slowly
Solving the ROI problem with AutoML
Summary
3
Chapter 2: Getting Started with Azure Machine Learning Service
Chapter 2: Getting Started with Azure Machine Learning Service
Technical requirements
Creating your first AMLS workspace
Building compute to run your AutoML jobs
Working with data in AMLS
Understanding how AutoML works on Azure
Summary
4
Chapter 3: Training Your First AutoML Model
Chapter 3: Training Your First AutoML Model
Technical requirements
Loading data into AMLS for AutoML
Creating an AutoML solution
Interpreting your AutoML results
Explaining your AutoML model
Obtaining better AutoML performance
Summary
5
Section 2: AutoML for Regression, Classification, and Forecasting – A Step-by-Step Guide
Section 2: AutoML for Regression, Classification, and Forecasting – A Step-by-Step Guide
6
Chapter 4: Building an AutoML Regression Solution
Chapter 4: Building an AutoML Regression Solution
Technical requirements
Preparing data for AutoML regression
Training an AutoML regression model
Registering your trained regression model
Fine-tuning your AutoML regression model
Summary
7
Chapter 5: Building an AutoML Classification Solution
Chapter 5: Building an AutoML Classification Solution
Technical requirements
Prepping data for AutoML classification
Training an AutoML classification model
Registering your trained classification model
Training an AutoML multiclass model
Fine-tuning your AutoML classification model
Summary
8
Chapter 6: Building an AutoML Forecasting Solution
Chapter 6: Building an AutoML Forecasting Solution
Technical requirements
Prepping data for AutoML forecasting
Training an AutoML forecasting model
Registering your trained forecasting model
Fine-tuning your AutoML forecasting model
Summary
9
Chapter 7: Using the Many Models Solution Accelerator
Chapter 7: Using the Many Models Solution Accelerator
Technical requirements
Installing the many models solution accelerator
Prepping data for many models
Training many models simultaneously
Scoring new data for many models
Improving your many models results
Summary
10
Section 3: AutoML in Production – Automating Real-Time and Batch Scoring Solutions
Section 3: AutoML in Production – Automating Real-Time and Batch Scoring Solutions
11
Chapter 8: Choosing Real-Time versus Batch Scoring
Chapter 8: Choosing Real-Time versus Batch Scoring
Technical requirements
Architecting batch scoring solutions
Architecting real-time scoring solutions
Determining batch versus real-time scoring scenarios
Summary
12
Chapter 9: Implementing a Batch Scoring Solution
Chapter 9: Implementing a Batch Scoring Solution
Technical requirements
Creating an ML pipeline
Creating a parallel scoring pipeline
Creating an AutoML training pipeline
Triggering and scheduling your ML pipelines
Summary
13
Chapter 10: Creating End-to-End AutoML Solutions
Chapter 10: Creating End-to-End AutoML Solutions
Technical requirements
Connecting AMLS to ADF
Scheduling a machine learning pipeline in ADF
Transferring data using ADF
Automating an end-to-end scoring solution
Automating an end-to-end training solution
Summary
14
Chapter 11: Implementing a Real-Time Scoring Solution
Chapter 11: Implementing a Real-Time Scoring Solution
Technical requirements
Creating real-time endpoints through the UI
Creating real-time endpoints through the SDK
Improving performance on your AKS cluster
Summary
15
Chapter 12: Realizing Business Value with AutoML
Chapter 12: Realizing Business Value with AutoML
Technical requirements
Architecting AutoML solutions
Visualizing AutoML modeling results
Explaining AutoML results to your business
Using AutoML in other Microsoft products
Realizing business value
Summary
Why subscribe?
16
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Creating a parallel scoring pipeline

Standard ML pipelines work just fine for the majority of ML use cases, but when you need to score a large amount of data at once, you need a more powerful solution. That's where ParallelRunStep comes in. ParallelRunStep is Azure's answer to scoring big data in batch. When you use ParallelRunStep, you leverage all of the cores on your compute cluster simultaneously.

Say you have a compute cluster consisting of eight Standard_DS3_v2 virtual machines. Each Standard_DS3_v2 node has four cores, so you can perform 32 parallel scoring processes at once. This parallelization essentially lets you score data many times faster than if you used a single machine. Furthermore, it can easily scale vertically (increasing the size of each virtual machine in the cluster) and horizontally (increasing the node count).

This section will allow you to become a big data scientist who can score large batches of data. Here, you will again be using simulated...

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