Book Image

Solutions Architect’s Handbook - Second Edition

By : Saurabh Shrivastava, Neelanjali Srivastav
4 (2)
Book Image

Solutions Architect’s Handbook - Second Edition

4 (2)
By: Saurabh Shrivastava, Neelanjali Srivastav

Overview of this book

Becoming a solutions architect requires a hands-on approach, and this edition of the Solutions Architect's Handbook brings exactly that. This handbook will teach you how to create robust, scalable, and fault-tolerant solutions and next-generation architecture designs in a cloud environment. It will also help you build effective product strategies for your business and implement them from start to finish. This new edition features additional chapters on disruptive technologies, such as Internet of Things (IoT), quantum computing, data engineering, and machine learning. It also includes updated discussions on cloud-native architecture, blockchain data storage, and mainframe modernization with public cloud. The Solutions Architect's Handbook provides an understanding of solution architecture and how it fits into an agile enterprise environment. It will take you through the journey of solution architecture design by providing detailed knowledge of design pillars, advanced design patterns, anti-patterns, and the cloud-native aspects of modern software design. By the end of this handbook, you'll have learned the techniques needed to create efficient architecture designs that meet your business requirements.
Table of Contents (22 chapters)
20
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21
Index

What is machine learning?

ML drives better customer experiences, more efficient business operations, and faster, more accurate decision making. With the rise in compute power and the proliferation of data, ML has moved from the periphery to be a core differentiator for businesses and organizations across industries. ML use cases can apply to most businesses, like personalized product and content recommendations, contact center intelligence, virtual identity verification, and intelligent document processing. And there are customized use cases built for a specific industry—like clinical trials in pharma or assembly line quality control in manufacturing.

Let's say your company wants to send marketing offers to potential customers for a new toy launch, and you have been tasked to develop a system to identify who to target for the marketing campaign. Your customer base could be millions of users to which you need to apply predictive analytics, and ML can help you solve...