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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

Request decomposition

The ABC-Monolith function request flow has already been identified and shown in Figure 1.3. We will now see how this flow is going to work in the ABC-MSA.

In the ABC-MSA, the sagas are programmed and configured in the centralized orchestrator. The orchestrator will initiate separate API calls to each service in the saga, in either a synchronous or asynchronous fashion, depending on the defined workflow, and wait for a response from each API call to determine what other API call(s) to initiate next and how.

The following diagram shows how the workflow would be in the ABC-MSA. Please note that all API calls in our scenario are being initiated from the orchestrator. As you can see from the sequence number, there are some API calls initiated in parallel, and in some other cases, the orchestrator decides the next course of action based on the response it receives from a previously executed service.

Figure 2.8: The ABC-MSA workflow

Figure 2.8: The ABC-MSA workflow

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Machine Learning in Microservices
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