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Key benefits

Explore the various AWS services that make automated machine learning easier

Recognize the role of DevOps and MLOps methodologies in pipeline automation

Get acquainted with additional AWS services such as Step Functions, MWAA, and more to overcome automation challenges

Description

AWS provides a wide range of solutions to help automate a machine learning workflow with just a few lines of code. With this practical book, you'll learn how to automate a machine learning pipeline using the various AWS services. Automated Machine Learning on AWS begins with a quick overview of what the machine learning pipeline/process looks like and highlights the typical challenges that you may face when building a pipeline. Throughout the book, you'll become well versed with various AWS solutions such as Amazon SageMaker Autopilot, AutoGluon, and AWS Step Functions to automate an end-to-end ML process with the help of hands-on examples. The book will show you how to build, monitor, and execute a CI/CD pipeline for the ML process and how the various CI/CD services within AWS can be applied to a use case with the Cloud Development Kit (CDK). You'll understand what a data-centric ML process is by working with the Amazon Managed Services for Apache Airflow and then build a managed Airflow environment. You'll also cover the key success criteria for an MLSDLC implementation and the process of creating a self-mutating CI/CD pipeline using AWS CDK from the perspective of the platform engineering team. By the end of this AWS book, you'll be able to effectively automate a complete machine learning pipeline and deploy it to production.

Who is this book for?

This book is for the novice as well as experienced machine learning practitioners looking to automate the process of building, training, and deploying machine learning-based solutions into production, using both purpose-built and other AWS services. A basic understanding of the end-to-end machine learning process and concepts, Python programming, and AWS is necessary to make the most out of this book.

What you will learn

Employ SageMaker Autopilot and Amazon SageMaker SDK to automate the machine learning process

Understand how to use AutoGluon to automate complicated model building tasks

Use the AWS CDK to codify the machine learning process

Create, deploy, and rebuild a CI/CD pipeline on AWS

Build an ML workflow using AWS Step Functions and the Data Science SDK

Leverage the Amazon SageMaker Feature Store to automate the machine learning software development life cycle (MLSDLC)

Discover how to use Amazon MWAA for a data-centric ML process

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Getting Started with Amazon SageMaker Studio

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Amazon verified reviews

Ashish Patel Jun 24, 2022

👉 It explains how to automatically configure a practical step for the ML process, its importance and an important governance factor to consider when performing this automation, and how to use the various AWS features that drive ML products to production.👉 The AutoML system, which aims to automate end-to-end ML model development, is highly demanding. Whereas this Amazon Sage Maker autopilot framework allows us to perform this critical steps in the typical ML process, which includes data mining, algorithm selection, model training, and model optimization.👉 In the Practise of AutoML, Autogluon provides an AutoML methodology that focuses on automated stack ensemble, deep learning, and real-world applications spanning images, text, and tabular data. We can wrap the ML application with SageMaker BYOC (bring your own container) or AWS Deep Learning Container services.👉 The CI / CD pipeline is the backbone of modern software development lifecycle (SDLC) and machine learning lifecycle (MLSDLC) automation. AWS CDK help in CI / CD approaches to machine learning allow you to scale ML in your organization, maintain a balanced development and production environment, and perform version control, on-demand testing, and ultimately automation.👉 Each CI / CD Pipeline has some limitations, such as the ML model process (from the point of view of ML practitioners) and all the paths to automated model deployment (from the perspective of application development and operation teams) Some AWS services, such as AWS CodePipeline and AWS CodeCommit, help to overcome this.👉 AWS Step Functions lets you build resilient workflows using AWS services such as Amazon Dynamodib, AWS Landa, and Amazon Sage Maker. In the Sagemaker Pipeline AWS step Function, you can organize end-to-end machine learning workflows that include data pre-processing, post-processing, feature engineering, data validation, and sample evaluation on Amazon SageMaker.👉 Introducing DataCentric Approach with Apache Airflow, it helps multiple Amazon SageMaker operators, whom are available with Airflow, including model training, hyperparameter tuning, model deployment, and batch transform. This allows you to use the same orchestration tool to manage ML workflows with tasks running on Amazon SageMaker.👉 ML software development life cycle (MLSDLC) introducing the six-phase flow of this : Plan ➡️ Design ➡️ Build ➡️ Test ➡️ Deploy ➡️ Maintain

Amazon Verified review

Guangping zhang May 05, 2022

Automated machine learning (AutoML) is more and more important, several automated ML services or libraries including AWS AutoML appeared recently. This book (Automated MachineLearning on AWS) is about AWS autoML, it fully introduced the most important applications of AWS autoML.This book first introduces a Continuous Integration and Continuous Delivery (CI/CD) methodology. Then, it uses chapters to introduce automating the ML Process and how to build ML workflow using Apache Airflow and Amazon Managed Workflows.At last, the book introduces ML Software development life cycle (MLSDLC) and the application.I think It's a very good book for the customers who are interested in learning AWS automated machine learning.

Devanshu Jul 25, 2022

This is a remarkable book that can help all data science enthusiasts and AWS practitioners. This will help to a larger audience who works and is willing to work either as an ML engineer or the Could practitioners. This is a must to have a look book.

Sireesha Muppala Apr 16, 2022

This is an awesome book for anyone looking to move beyond the ML model development basics and operationalize machine learning to achieve business value. Author's experience working with various customers comes through as he expertly discusses ML theory, business use cases and takes the reader on a journey of various tools to automate building an ML application. The GitHub repository accompanying the book is a great resource to gain hands-on expertise of the concepts covered. As the ML field continues to evolve, the timeless ideas covered such as automation and CI/CD will help organizations deliver repeated business value.

Amazon Customer Oct 12, 2022

Before commenting on the contents of Automated Machine Learning on AWS, I must express how much I have to appreciate the fact that this book was newly published. As web services like AWS change the UIs or features of some of its services fairly frequently, this book’s examples manage to provide accurate hands-on instructions to toggling the relevant AWS services. Content-wise, the book is structured in a progressive manner. It first introduces how to perform CRISP-DM methodology using AutoPilot and AutoGluon, and introduces their pros and cons. Then, in section 2, it provides solutions to address the cons of the solutions mentioned in section 1, along with introducing the concepts and hands-ons for CI/CD methodology. As the topics progress further, more AWS solutions are mentioned tailored to different DS/ML development styles. So far I’ve read over half of the book and have felt more confident exercising my MLOps in the workplace. I would recommend this book for DS and MLE who wants to explore more cloud solutions for end-to-end ML projects. However, it might be a bit challenging for readers who have no prior experience with AWS and the common ML deployment tools such as Docker and Kubernetes. Fortunately, the author has kindly provided links whenever there are new AWS services or concepts being introduced, to facilitate our learning. It is daunting to learn ML deployment, but with the help of Automated Machine Learning on AWS, the journey will be easier.

Automated Machine Learning on AWS: Fast-track the development of your production-ready machine learning applications the AWS way

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