Book Image

Solutions Architect's Handbook

By : Saurabh Shrivastava, Neelanjali Srivastav
Book Image

Solutions Architect's Handbook

By: Saurabh Shrivastava, Neelanjali Srivastav

Overview of this book

Becoming a solutions architect gives you the flexibility to work with cutting-edge technologies and define product strategies. This handbook takes you through the essential concepts, design principles and patterns, architectural considerations, and all the latest technology that you need to know to become a successful solutions architect. This book starts with a quick introduction to the fundamentals of solution architecture design principles and attributes that will assist you in understanding how solution architecture benefits software projects across enterprises. You'll learn what a cloud migration and application modernization framework looks like, and will use microservices, event-driven, cache-based, and serverless patterns to design robust architectures. You'll then explore the main pillars of architecture design, including performance, scalability, cost optimization, security, operational excellence, and DevOps. Additionally, you'll also learn advanced concepts relating to big data, machine learning, and the Internet of Things (IoT). Finally, you'll get to grips with the documentation of architecture design and the soft skills that are necessary to become a better solutions architect. By the end of this book, you'll have learned techniques to create an efficient architecture design that meets your business requirements.
Table of Contents (18 chapters)

What is ML?

Let's say your company wants to send marketing offers to potential customers for a new toy launch and you have been tasked to come up with a system to identify whom 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 to solve such a complex problem.

ML is about using technology to discover trends and patterns and compute mathematical predictive models based on past factual data. ML can help to solve complex problems such as the following:

  • When you may not know how to create complex code rules to make a decision. For example, if you want to recognize people's emotions in image and speech, there are just no easy ways to code the logic to achieve that.
  • When you need human expertise to analyze a large amount of data for decision-making, but the volume of data is too large for a human to do it efficiently. For example, with spam detection, while a human can do it, the...