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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

In this chapter, we looked at the different key components that make up a machine learning pipeline.

From there, we looked in detail at the interfaces that make up the components. We started with the transform interface, which is responsible for the data aspect of the pipeline. It takes the data and applies different types of data transformation that allow us to maintain clean and stable data, which we can later use in our machine learning model.

After our transformation stage, we start creating our model in the fit interface. Here, we can use the prebuilt models that the libraries and packages offer to initialize our models. Due to the ease of creating models, it is a good practice to test different types of models to see which model performs the best based on our data.

Once we have created our model, we can begin the actual training of our model. We need to split our data into training and test sets to allow our model to understand the relationship in our data. From...

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Machine Learning in Microservices
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