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AWS for Solutions Architects

You're reading from   AWS for Solutions Architects The definitive guide to AWS Solutions Architecture for migrating to, building, scaling, and succeeding in the cloud

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Product type Paperback
Published in Apr 2023
Publisher Packt
ISBN-13 9781803238951
Length 692 pages
Edition 2nd Edition
Tools
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Authors (4):
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Neelanjali Srivastav Neelanjali Srivastav
Author Profile Icon Neelanjali Srivastav
Neelanjali Srivastav
Saurabh Shrivastava Saurabh Shrivastava
Author Profile Icon Saurabh Shrivastava
Saurabh Shrivastava
Alberto Artasanchez Alberto Artasanchez
Author Profile Icon Alberto Artasanchez
Alberto Artasanchez
Imtiaz Sayed Imtiaz Sayed
Author Profile Icon Imtiaz Sayed
Imtiaz Sayed
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Table of Contents (19) Chapters Close

AWS for Solutions Architects, Second Edition: Design your cloud infrastructure by implementing DevOps, containers, and Amazon Web Services
1 Understanding AWS Principles and Key Characteristics FREE CHAPTER 2 Understanding AWS Well-Architected Framework and Getting Certified 3 Leveraging the Cloud for Digital Transformation 4 Networking in AWS 5 Storage in AWS – Choosing the Right Tool for the Job 6 Harnessing the Power of Cloud Computing 7 Selecting the Right Database Service 8 Best Practices for Application Security, Identity, and Compliance 9 Dive efficiency with Cloud Operation Automation and DevOps in AWS 10 Bigdata and streaming data processing in AWS 11 Datawarehouse, Data Query and Visualization in AWS 12 Machine Learning, IoT, and Blockchain in AWS 13 Containers in AWS 14 Microservice and Event-Driven Architectures 15 Domain-Driven Design 16 Data Lake Patterns – Integrating Your Data across the Enterprise 17 Availability, Reliability, and Scalability Patterns 18 AWS Hands-On Lab and Use Case

AWS ML infrastructure and framework

AWS provides a variety of infrastructure services for building and deploying machine learning (ML) models. Some of the key services include

Amazon EC2 for ML workload: AWS provides a variety of EC2 instance types that can be used for ML workloads. Depending on the workload's needs, these instances can be configured with different amounts of CPU, memory, and GPU resources. For example, the P3 and G4 instances are designed explicitly for ML workloads and provide high-performance GPU resources.

Amazon Elastic Inference: It is a service that allows you to attach GPU resources to Amazon EC2 or Amazon SageMaker instances to accelerate machine learning inference workloads.

AWS Inferentia: AWS provides a custom-built chip called Inferentia that can be used to perform low-latency, high-throughput inferences on deep learning workloads. It is designed to provide high performance at a low cost and can be used with Amazon SageMaker.

AWS Trainium: AWS Trainium...

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