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)
1
Part 1: Introduction to CV on AWS and Amazon Rekognition
5
Part 2: Applying CV to Real-World Use Cases
9
Part 3: CV at the edge
12
Part 4: Building CV Solutions with Amazon SageMaker
15
Part 5: Best Practices for Production-Ready CV Workloads

Developing an MLOps strategy

Now that we’ve discussed the best practices to apply to each stage of the ML life cycle, how can we automate and streamline these processes? We can accomplish this by incorporating MLOps. What is MLOps? MLOps is related to DevOps in concept, where both practices focus on automating and accelerating applications or systems from development to production. The difference between the two is that the goal of DevOps is to deliver software applications, while the goal of MLOps is to deliver ML models. MLOps allows you to automate your ML workflows and create repeatable mechanisms to accelerate the processes for building, training, deploying, and managing ML models. You can leverage tools such as workflow automation software for orchestration and continuous integration/continuous delivery (CI/CD) of your ML systems. Other components of MLOps include tracking lineage using a Model Registry. Also, monitoring models in production and providing corrective actions...