| Description |
1 online resource |
| Contents |
Cover -- Title Page -- Copyright and Credits -- Contributors -- Table of Contents -- Preface -- Part 1: Introduction to CV on AWS and Amazon Rekognition -- Chapter 1: Computer Vision Applications and AWS AI/ML Services Overview -- Technical requirements -- Understanding CV -- CV architecture and applications -- Data processing and feature engineering -- Data labeling -- Solving business challenges with CV -- Contactless check-in and checkout -- Video analysis -- Content moderation -- CV at the edge -- Exploring AWS AI/ML services -- AWS AI services -- Amazon SageMaker |
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Setting up your AWS environment -- Creating an Amazon SageMaker Jupyter notebook instance -- Summary -- Chapter 2: Interacting with Amazon Rekognition -- Technical requirements -- The Amazon Rekognition console -- Using the Label detection demo -- Examining the API request -- Examining the API response -- Other demos -- Monitoring Amazon Rekognition -- Quick recap -- Detecting Labels using the API -- Uploading the images to S3 -- Initializing the boto3 client -- Detect the Labels -- Using the Label information -- Using bounding boxes -- Quick recap -- Cleanup -- Summary |
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Chapter 3: Creating Custom Models with Amazon Rekognition Custom Labels -- Technical requirements -- Introducing Amazon Rekognition Custom Labels -- Benefits of Amazon Rekognition Custom Labels -- Creating a model using Rekognition Custom Labels -- Deciding the model type based on your business goal -- Creating a model -- Improving the model -- Starting your model -- Analyzing an image -- Stopping your model -- Building a model to identify Packt's logo -- Step 1 -- Collecting your images -- Step 2 -- Creating a project -- Step 3 -- Creating training and test datasets |
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Step 4 -- Adding labels to the project -- Step 5 -- Drawing bounding boxes on your training and test datasets -- Step 6 -- Training your model -- Validating that the model works -- Step 1 -- Starting your model -- Step 2 -- Analyzing an image with your model -- Step 3 -- Stopping your model -- Summary -- Part 2: Applying CV to Real-World Use Cases -- Chapter 4: Using Identity Verification to Build a Contactless Hotel Check-In System -- Technical requirements -- Prerequisites -- Creating the image bucket -- Uploading the sample images -- Creating the profile table -- Introducing collections |
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Creating a collection -- Describing a collection -- Deleting a collection -- Quick recap -- Describing the user journeys -- Registering a new user -- Authenticating a user -- Registering a new user with an ID card -- Updating the user profile -- Implementing the solution -- Checking image quality -- Indexing face information -- Search existing faces -- Quick recap -- Supporting ID cards -- Reading an ID card -- Using the CompareFaces API -- Quick recap -- Guidance for identity verification on AWS -- Solution overview -- Deployment process -- Cleanup -- Summary |
| Summary |
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 |
| SUBJECT |
Amazon Web Services
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| Subject |
Computer vision -- Computer programs
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Cloud computing.
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Web services.
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Artificial intelligence.
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artificial intelligence.
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Artificial intelligence
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Cloud computing
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Computer vision -- Computer programs
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Web services
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| Form |
Electronic book
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| Author |
Bachmeier, Nate, author
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Rao, Jay, author
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| ISBN |
9781803248202 |
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1803248203 |
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