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Machine Learning in Microservices

Machine Learning in Microservices

By : Mohamed Osam Abouahmed, Omar Ahmed
4.7 (10)
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Machine Learning in Microservices

Machine Learning in Microservices

4.7 (10)
By: Mohamed Osam Abouahmed, Omar Ahmed

Overview of this book

With the rising need for agile development and very short time-to-market system deployments, incorporating machine learning algorithms into decoupled fine-grained microservices systems provides the perfect technology mix for modern systems. Machine Learning in Microservices is your essential guide to staying ahead of the curve in this ever-evolving world of technology. The book starts by introducing you to the concept of machine learning microservices architecture (MSA) and comparing MSA with service-based and event-driven architectures, along with how to transition into MSA. Next, you’ll learn about the different approaches to building MSA and find out how to overcome common practical challenges faced in MSA design. As you advance, you’ll get to grips with machine learning (ML) concepts and see how they can help better design and run MSA systems. Finally, the book will take you through practical examples and open source applications that will help you build and run highly efficient, agile microservices systems. By the end of this microservices book, you’ll have a clear idea of different models of microservices architecture and machine learning and be able to combine both technologies to deliver a flexible and highly scalable enterprise system.
Table of Contents (18 chapters)
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1
Part 1: Overview of Microservices Design and Architecture
5
Part 2: Overview of Machine Learning Algorithms and Applications
10
Part 3: Practical Guide to Deploying Machine Learning in MSA Systems

Function decomposition

The first step in refactoring the ABC-monolith is to create the microservices based on the system functions we previously identified. This is a straightforward mapping between the existing functions and the microservices.

The key point here is that, by looking only at each function by itself without considering any function calls or data connections, you need to be as granular as possible in your function decomposition.

At first glance, the notify_user() function is doing too many things for a microservice, displaying a web user message status/update, notifying the user by email, and/or notifying the user by SMS. Each of these functions can have its own rules, design, issues, and concerns. Splitting the notify_user() function into three functions is a better approach from an MSA perspective to achieve the separation of concerns.

Accordingly, we split the notify_user() function into one function for handling web messages and notifications, one for handling...

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Machine Learning in Microservices
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