Automated Machine Learning Hyperparameter optimization, neural architecture search, and algorithm selection

Get to grips with automated machine learning and adopt a hands-on approach to AutoML implementation and associated methodologies

Key Features

Get up to speed with AutoML using OSS, Azure, AWS, GCP, or any platform of your choice

Eliminate mundane tasks in data eeering and reduce human errors in machine learning models

Find out how you can make machine learning accessible for all users to promote decentralized processes

Book Description

Every machine learning eeer 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 and effort.

This book reviews the underlying techniques of automated feature eeering, 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, 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 -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 eeering tasks.

What you will learn

Explore AutoML fundamentals, underlying methods, and techniques

Assess AutoML aspects such as algorithm selection, auto featurization, and hyperparameter tuning in an applied scenario

Find out the difference between cloud and operations support systems (OSS)

Implement AutoML in enterprise cloud to deploy ML models and pipelines

Build explainable AutoML pipelines with transparency

Understand automated feature eeering and series forecasting

Automate data science modeling tasks to implement ML solutions easily and focus on more complex problems

Who this book is for

Citizen data scientists, machine learning developers, artificial intelligence enthusiasts, or anyone looking to automatically build machine learning models using the features offered by open source tools, Microsoft Azure Machine Learning, AWS, and Google Cloud Platform will find this book useful. Bner-level knowledge of building ML models is required to get the best out of this book. Prior experience in using Enterprise cloud is beneficial.

Table of Contents

A Lap around Automated Machine Learning

Automated Machine Learning, Algorithms, and Techniques

Automated Machine Learning with Open Source Tools and Libraries

Getting Started with Azure Machine Learning

Automated Machine Learning with Microsoft Azure

Machine Learning with Web Services

Doing Automated Machine Learning with SageMaker Autopilot

Machine Learning with Google Cloud Platform

Automated Machine Learning with GCP Cloud AutoML

AutoML in the Enterprise