Book Image

Automated Machine Learning

By : Adnan Masood
Book Image

Automated Machine Learning

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)
1
Section 1: Introduction to Automated Machine Learning
5
Section 2: AutoML with Cloud Platforms
12
Section 3: Applied Automated Machine Learning

AWS JumpStart

In Dec 2020, Amazon announced SageMaker JumpStart as a capability to access pre-built model repositories also called model zoos to accelerate model development. Integrated as apart of Amazon SageMaker, JumpStart provides pre-built templates for predictive maintenance, computer vision, autonomous driving, fraud detection, credit risk prediction, OCR for extracting and analyze data from documents, churn prediction, and personalized recommendations.

JumpStart provides an excellent starting point for developers to use these pre-existing templates to JumpStart (pun intended) their development. These accelerator and starter kits are available on GitHub here. https://github.com/awslabs/ and provide recipes and best practices to use Amazon SageMaker model development and deployment mechanisms.

Further details on using AWS JumpStart can be found here. https://docs.aws.amazon.com/sagemaker/latest/dg/studio-jumpstart.html