Book Image

Computer Vision on AWS

By : Lauren Mullennex, Nate Bachmeier, Jay Rao
Book Image

Computer Vision on AWS

By: Lauren Mullennex, Nate Bachmeier, Jay Rao

Overview of this book

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.
Table of Contents (21 chapters)
Part 1: Introduction to CV on AWS and Amazon Rekognition
Part 2: Applying CV to Real-World Use Cases
Part 3: CV at the edge
Part 4: Building CV Solutions with Amazon SageMaker
Part 5: Best Practices for Production-Ready CV Workloads

Using AWS Lambda to automate the workflow

AWS Lambda is a serverless, event-driven computing service that lets you run code for virtually any application or backend service (that’s Linux-compatible). Under the covers, it uses a purpose-built operating system called Firecracker to scale your code elastically within milliseconds. This capability allows Lambda to support burst traffic patterns and AWS to charge per millisecond that your code is running!

There are three phases to a Lambda function’s life cycle: Init, Invoke, and Shutdown. During the Init phase, your code should fetch configuration information and prepare any stateless clients. Next, the Lambda service will invoke the lambda_handler entry point one or more times. After processing all pending events, the service eventually releases the underlying Firecracker micro-virtual machine.

Now that you’ve had a 30-second crash course on the service, let’s use it to operationalize the entire video...