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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

Building multiclass classification

Unlike regression models that produce a continuous output, models are considered classification models when they produce a finite output. Some examples include email spam detection, image classification, and speech recognition.

Classification models are considered versatile since they can apply to both supervised and unsupervised learning while regression models are mostly used for supervised learning. There are some regression models (such as logistic regression and support vector machine) that are also considered classification models since they use a threshold to split the output of continuous values into different categories.

Unsupervised learning is a common application used in today’s market. Although supervised learning usually performs better and provides meaningful results since we know the expected output, the majority of the data we collect is unlabeled. It costs companies time and money for human experts to sift through the...

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Machine Learning in Microservices
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