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  • Book Overview & Buying Machine Learning in Microservices
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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

Summary

This chapter discussed how we can implement machine learning and deep learning in our MSA.

We first looked into the different use cases for how machine learning can be used to build an intelligent MSA. The uses cases can be grouped into four categories:

  • System Load Prediction
  • System Decay Prediction
  • System Security
  • System Resource Planning

We discussed each category and what role it plays when looking into creating an intelligent MSA.

From there, we started looking into using supervised machine learning to create a pattern analysis model where it can learn our MSA and create a performance baseline model. Using this, we can determine whether our microservice performance is abnormal. We can then use this to either perform actions based on a threshold or use this baseline to build a more advanced model.

Along with our supervised machine learning model, we can use deep learning to create a more sophisticated model, such as autoencoders, to find...

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Machine Learning in Microservices
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