Develop scalable computer vision solutions for real-world business problems and discover scaling, cost reduction, security, and bias mitigation best practices with AWS AI/ML services
Purchase of the print or Kindle book includes a free PDF eBook
Key Features
- Learn how to quickly deploy and automate end-to-end CV pipelines on AWS
- Implement design principles to mitigate bias and scale production of CV workloads
- Work with code examples to master CV concepts using AWS AI/ML services
Book Description
Computer vision (CV) is a field of artificial intelligence that helps transform visual data into actionable insights to solve a wide range of business challenges. This book provides prescriptive guidance to anyone looking to learn how to approach CV problems for quickly building and deploying production-ready models.
You'll begin by exploring the applications of CV and the features of Amazon Rekognition and Amazon Lookout for Vision. The book will then walk you through real-world use cases such as identity verification, real-time video analysis, content moderation, and detecting manufacturing defects that'll enable you to understand how to implement AWS AI/ML services. As you make progress, you'll also use Amazon SageMaker for data annotation, training, and deploying CV models. In the concluding chapters, you'll work with practical code examples, and discover best practices and design principles for scaling, reducing cost, improving the security posture, and mitigating bias of CV workloads.
By the end of this AWS book, you'll be able to accelerate your business outcomes by building and implementing CV into your production environments with the help of AWS AI/ML services.
What you will learn
- Apply CV across industries, including e-commerce, logistics, and media
- Build custom image classifiers with Amazon Rekognition Custom Labels
- Create automated end-to-end CV workflows on AWS
- Detect product defects on edge devices using Amazon Lookout for Vision
- Build, deploy, and monitor CV models using Amazon SageMaker
- Discover best practices for designing and evaluating CV workloads
- Develop an AI governance strategy across the entire machine learning life cycle
Who this book is for
If you are a machine learning engineer or data scientist looking to discover best practices and learn how to build comprehensive CV solutions on AWS, this book is for you. Knowledge of AWS basics is required to grasp the concepts covered in this book more effectively. A solid understanding of machine learning concepts and the Python programming language will also be beneficial.
Table of Contents
- Product Information Document
- Computer Vision Applications and AWS AI/ML Overview
- Interacting with Amazon Rekognition
- Creating Custom Models with Amazon Rekognition Custom Labels
- Using Identity Verification to Build a Contactless Hotel Check-In System
- Automating a Video Analysis Pipeline
- Moderating Content with AWS AI Services
- Introducing Amazon Lookout for Vision
- Detecting Manufacturing Defects using CV at the Edge
- Labeling Data with Amazon SageMaker Ground Truth
- Using Amazon SageMaker for Computer Vision
- Integrating Human-in-the-Loop with Amazon Augmented AI (A2I)
- Best Practices for Designing an End-to-End CV Pipeline
- Applying AI Governance in CV
Lauren Mullennex is a Senior AI/ML Specialist Solutions Architect at AWS. She has broad experience in infrastructure, DevOps, and cloud architecture across multiple industries. She has published multiple AWS AI/ML blogs, spoken at AWS conferences, and focuses on developing solutions using CV and MLOps.
Nate Bachmeier is a Principal Solutions Architect at AWS (Ph.D. CS, MBA). He nomadically explores the world one cloud integration at a time, focusing on the Financial Service industry.
Jay Rao is a Principal Solutions Architect at AWS. He enjoys providing technical and strategic guidance to customers and helping them design and implement solutions.
