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

Product type Paperback

Published in Apr 2023

Publisher Packt

ISBN-13 9781803238951

Length 692 pages

Edition 2nd Edition

Tools

AWS

Concepts

Cloud Computing

Authors (4):

Neelanjali Srivastav

Saurabh Shrivastava

Alberto Artasanchez

Imtiaz Sayed

View More author details

Table of Contents (19) Chapters

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...