ISBN: 9781801078689 - computer vision on aws: build and deploy real-world cv solutions with amazon rekognition, lookout for vision, and sagemaker (16 Ergebnisse)

Zustand: New.

Paperback or Softback. Zustand: New. Computer Vision on AWS: Build and deploy real-world CV solutions with Amazon Rekognition, Lookout for Vision, and SageMaker. Book.

Zustand: As New. Unread book in perfect condition.

Zustand: New.

Zustand: New.

Zustand: As New. Unread book in perfect condition.

paperback. Zustand: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Paperback. Zustand: new. New Copy. Customer Service Guaranteed.

PAP. Zustand: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Zustand: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Zustand: New. Print on Demand.

Zustand: New. Print on Demand.

Zustand: New. PRINT ON DEMAND.

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Scale up your Windows containers seamlessly on AWS powered by field-proven expertise and best practices on Amazon ECS, EKS, and FargatePurchase of the print or Kindle book includes a free PDF Elektronisches BuchKey Features:Learn how to quickly deploy and automate end-to-end CV pipelines on AWSImplement design principles to mitigate bias and scale production of CV workloadsWork with code examples to master CV concepts using AWS AI/ML servicesBook 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 mediaBuild custom image classifiers with Amazon Rekognition Custom LabelsCreate automated end-to-end CV workflows on AWSDetect product defects on edge devices using Amazon Lookout for VisionBuild, deploy, and monitor CV models using Amazon SageMakerDiscover best practices for designing and evaluating CV workloadsDevelop an AI governance strategy across the entire machine learning life cycleWho 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.…

Taschenbuch. Zustand: Neu. Computer Vision on AWS | Build and deploy real-world CV solutions with Amazon Rekognition, Lookout for Vision, and SageMaker | Lauren Mullennex (u. a.) | Taschenbuch | Englisch | 2023 | Packt Publishing | EAN 9781801078689 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. …

Paperback / softback. Zustand: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.