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.

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Spis treści

Automated Machine Learning on AWS. Fast-track the development of your production-ready machine learning applications the AWS way eBook -- spis treści

• 1. Getting Started with Automated Machine Learning on AWS
• 2. Automating Machine Learning Model Development Using SageMaker Autopilot
• 3. Automating Complicated Model Development with AutoGluon
• 4. Continuous Integration and Continuous Delivery (CI/CD) for Machine Learning
• 5. Continuous Deployment of a Production ML Model
• 6. Automating the Machine Learning Process Using AWS Step Functions
• 7. Building the ML Workflow Using AWS Step Functions
• 8. Automating the Machine Learning Process Using Apache Airflow
• 9. Building the ML Workflow Using Amazon Managed Workflows for Apache Airflow
• 10. An Introduction to the Machine Learning Software Development Lifecycle (MLSDLC)
• 11. Continuous Integration, Deployment, and Training for the MLSDLC