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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

The differences between artificial intelligence, machine learning, and deep learning

Despite the recent rise in popularity of artificial intelligence and machine learning, the field of artificial intelligence has been around since the 1960s. With different sub-fields emerging, it is important to be able to differentiate between them and understand them and what they entail.

To start, artificial intelligence is the overarching field that encompasses all the sub-fields we see today, such as machine learning, deep learning, and more. Any system that perceives or receives information from its environment and carries out an action to maximize the reward or achieve its goal is considered to be an artificially intelligent machine.

This is commonly used today when it comes to robotics. Most of our machines are designed so that they can capture data using their sensors, such as cameras, sonars, or gyroscopes, and use the data captured to respond to a particular task most efficiently....

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Machine Learning in Microservices
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