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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

Machine learning system components

There are many moving parts required in order to build a robust machine learning system. Starting from gathering data to deploying your model to the user, each plays a vital role in keeping the system dynamic and scalable. Here, we will briefly discuss the different stages in the machine learning system life cycle and the role they play. These stages can be edited in order to suit the model or application at hand.

The majority of machine learning systems include the following stages, with some other stages depending on business needs:

  • Data collection
  • Date preprocessing
  • Model training
  • Model testing
  • Model serving

Realistically, the majority of the time spent building machine learning systems is spent on the data. This is a key element in the process that can decide the effectiveness of your system since the model is dependent on the data it uses during training. Just like the human body, if you feed the model poor data...

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Machine Learning in Microservices
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