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Automated Machine Learning

Automated Machine Learning

By : Adnan Masood
4.5 (15)
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Automated Machine Learning

Automated Machine Learning

4.5 (15)
By: Adnan Masood

Overview of this book

Every machine learning engineer deals with systems that have hyperparameters, and the most basic task in automated machine learning (AutoML) is to automatically set these hyperparameters to optimize performance. The latest deep neural networks have a wide range of hyperparameters for their architecture, regularization, and optimization, which can be customized effectively to save time and effort. This book reviews the underlying techniques of automated feature engineering, model and hyperparameter tuning, gradient-based approaches, and much more. You'll discover different ways of implementing these techniques in open source tools and then learn to use enterprise tools for implementing AutoML in three major cloud service providers: Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform. As you progress, you’ll explore the features of cloud AutoML platforms by building machine learning models using AutoML. The book will also show you how to develop accurate models by automating time-consuming and repetitive tasks in the machine learning development lifecycle. By the end of this machine learning book, you’ll be able to build and deploy AutoML models that are not only accurate, but also increase productivity, allow interoperability, and minimize feature engineering tasks.
Table of Contents (15 chapters)
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Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the color images
Conventions used
Get in touch
Reviews
1
Section 1: Introduction to Automated Machine Learning
Section 1: Introduction to Automated Machine Learning
Free Chapter
2
Chapter 1: A Lap around Automated Machine Learning
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Chapter 1: A Lap around Automated Machine Learning
The ML development life cycle
Automated ML
How automated ML works
Democratization of data science
Debunking automated ML myths
Automated ML ecosystem
Open source platforms and tools
Commercial tools and platforms
Summary
Further reading
3
Chapter 2: Automated Machine Learning, Algorithms, and Techniques
Chapter 2: Automated Machine Learning, Algorithms, and Techniques
Automated ML – Opening the hood
Automated feature engineering
Hyperparameter optimization
Neural architecture search
Summary
Further reading
4
Chapter 3: Automated Machine Learning with Open Source Tools and Libraries
Chapter 3: Automated Machine Learning with Open Source Tools and Libraries
Technical requirements
The open source ecosystem for AutoML
Introducing TPOT
Introducing Featuretools
Introducing Microsoft NNI
Introducing auto-sklearn
AutoKeras
AutoGluon – the AutoML toolkit for deep learning
Summary
Further reading
5
Section 2: AutoML with Cloud Platforms
Section 2: AutoML with Cloud Platforms
6
Chapter 4: Getting Started with Azure Machine Learning
Chapter 4: Getting Started with Azure Machine Learning
Getting started with Azure Machine Learning
The Azure Machine Learning stack
Getting started with the Azure Machine Learning service
Modeling with Azure Machine Learning
Deploying and testing models with Azure Machine Learning
Summary
Further reading
7
Chapter 5: Automated Machine Learning with Microsoft Azure
Chapter 5: Automated Machine Learning with Microsoft Azure
AutoML in Microsoft Azure
Time series prediction using AutoML
Summary
Further reading
8
Chapter 6: Machine Learning with AWS
Chapter 6: Machine Learning with AWS
ML in the AWS landscape
Getting started with AWS ML
AWS SageMaker Autopilot
AWS JumpStart
Summary
Further reading
9
Chapter 7: Doing Automated Machine Learning with Amazon SageMaker Autopilot
Chapter 7: Doing Automated Machine Learning with Amazon SageMaker Autopilot
Technical requirements
Creating an Amazon SageMaker Autopilot limited experiment
Creating an AutoML experiment
Running the SageMaker Autopilot experiment and deploying the model
Building and running SageMaker Autopilot experiments from the notebook
Summary
Further reading
10
Chapter 8: Machine Learning with Google Cloud Platform
Chapter 8: Machine Learning with Google Cloud Platform
Getting started with the Google Cloud Platform services
AI and ML with GCP
Google Cloud AI Platform and AI Hub
Getting started with Google Cloud AI Platform
Automated ML with Google Cloud
Summary
Further reading
11
Chapter 9: Automated Machine Learning with GCP
Chapter 9: Automated Machine Learning with GCP
Getting started with Google Cloud AutoML Tables
Creating an AutoML Tables experiment
Understanding AutoML Tables model deployment
AutoML Tables with BigQuery public datasets
Automated machine learning for price prediction
Summary
Further reading
12
Section 3: Applied Automated Machine Learning
Section 3: Applied Automated Machine Learning
13
Chapter 10: AutoML in the Enterprise
Chapter 10: AutoML in the Enterprise
Does my organization need automated ML?
Automated ML – an accelerator for enterprise advanced analytics
Automated ML challenges and opportunities
Establishing trust – model interpretability and transparency in automated ML
Introducing automated ML in an organization
Call to action – where do I go next?
References and further reading
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Chapter 1: A Lap around Automated Machine Learning

"All models are wrong, but some are useful."

– George Edward Pelham Box FRS

"One of the holy grails of machine learning is to automate more and more of the feature engineering process."

– Pedro Domingos, A Few Useful Things to Know about Machine Learning

This chapter will provide an overview of the concepts, tools, and technologies surrounding automated Machine Learning (ML). This introduction hopes to provide both a solid overview for novices and serve as a reference for experienced ML practitioners. We will start by introducing the ML development life cycle while navigating through the product ecosystem and the data science problems it addresses, before looking at feature selection, neural architecture search, and hyperparameter optimization.

It's very plausible that you are reading this book on an e-reader that's connected to a website that recommended this manuscript based on your reading interests. We live in a world today where your digital breadcrumbs give telltale signs of not only your reading interests, but where you like to eat, which friend you like most, where you will shop next, whether you will show up to your next appointment, and who you would vote for. In this age of big data, this raw data becomes information that, in turn, helps build knowledge and insights into so-called wisdom.

Artificial Intelligence (AI) and its underlying implementations of ML and deep learning help us not only find the metaphorical needle in the haystack, but also to see the underlying trends, seasonality, and patterns in these large data streams to make better predictions. In this book, we will cover one of the key emerging technologies in AI and ML; that is, automated ML, or AutoML for short.

In this chapter, we will cover the following topics:

  • The ML development life cycle
  • Automated ML
  • How automated ML works
  • Democratization of data science
  • Debunking automated ML myths
  • Automated ML ecosystem (open source and commercial)
  • Automated ML challenges and limitations

Let's get started!

End of Section 1
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