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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)
Buy this Book
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Machine Learning in Microservices

Machine Learning in Microservices

4.7 (10)
By: Mohamed Osam Abouahmed, Omar Ahmed
Buy this Book

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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Preface
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Preface
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Who this book is for
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What this book covers
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To get the most out of this book
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Download the example code files
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Conventions used
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Get in touch
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Share Your Thoughts
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Download a free PDF copy of this book
1
Part 1: Overview of Microservices Design and Architecture
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Part 1: Overview of Microservices Design and Architecture
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2
Chapter 1: Importance of MSA and Machine Learning in Enterprise Systems
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Chapter 1: Importance of MSA and Machine Learning in Enterprise Systems
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Why microservices? Pros and cons
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Loosely versus tightly coupled monolithic systems
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Service-driven, EDA, and MSA hybrid model architecture
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DevOps in MSA
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Summary
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Chapter 2: Refactoring Your Monolith
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Chapter 2: Refactoring Your Monolith
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Identifying the system’s microservices
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The ABC monolith
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Function decomposition
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Data decomposition
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Request decomposition
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Summary
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Chapter 3: Solving Common MSA Enterprise System Challenges
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Chapter 3: Solving Common MSA Enterprise System Challenges
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MSA isolation using an ACL
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Using an API gateway
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Service catalogs and orchestrators
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Microservices aggregators
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Gateways versus orchestrators versus aggregators
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Microservices circuit breaker
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ABC-MSA enhancements
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Summary
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Part 2: Overview of Machine Learning Algorithms and Applications
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Part 2: Overview of Machine Learning Algorithms and Applications
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Chapter 4: Key Machine Learning Algorithms and Concepts
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Chapter 4: Key Machine Learning Algorithms and Concepts
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The differences between artificial intelligence, machine learning, and deep learning
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Common deep learning and machine learning libraries used in Python
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Building regression models
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Building multiclass classification
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Text sentiment analysis and topic modeling
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Pattern analysis and forecasting in machine learning
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Enhancing models using deep learning
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Summary
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Chapter 5: Machine Learning System Design
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Chapter 5: Machine Learning System Design
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Machine learning system components
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Fit and transform interfaces
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Train and serve interfaces
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Orchestration
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Summary
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Chapter 6: Stabilizing the Machine Learning System
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Chapter 6: Stabilizing the Machine Learning System
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Machine learning parameterization and dataset shifts
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The causes of dataset shifts
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Identifying dataset shifts
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Handling and stabilizing dataset shifts
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Summary
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Chapter 7: How Machine Learning and Deep Learning Help in MSA Enterprise Systems
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Chapter 7: How Machine Learning and Deep Learning Help in MSA Enterprise Systems
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Machine learning MSA enterprise system use cases
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Enhancing system supportability and time-to-resolution (TTR) with pattern analysis machine learning
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Implementing system self-healing with deep learning
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Summary
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Part 3: Practical Guide to Deploying Machine Learning in MSA Systems
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Part 3: Practical Guide to Deploying Machine Learning in MSA Systems
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Chapter 8: The Role of DevOps in Building Intelligent MSA Enterprise Systems
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Chapter 8: The Role of DevOps in Building Intelligent MSA Enterprise Systems
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DevOps and organizational structure alignment
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DevOps processes in enterprise MSA system operations
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Applying DevOps from the start to operations and maintenance
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Summary
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Chapter 9: Building an MSA with Docker Containers
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Chapter 9: Building an MSA with Docker Containers
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What are containers anyway, and why use them?
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Installing Docker
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Creating ABC-MSA containers
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ABC-MSA microservice inter-communication
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Summary
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Chapter 10: Building an Intelligent MSA Enterprise System
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Chapter 10: Building an Intelligent MSA Enterprise System
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The machine learning advantage
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Building your first AI microservice
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The intelligent MSA system in action
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Analyzing AI service operations
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Summary
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Chapter 11: Managing the New System’s Deployment – Greenfield versus Brownfield
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Chapter 11: Managing the New System’s Deployment – Greenfield versus Brownfield
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Deployment strategies
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Greenfield versus brownfield deployment
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Overcoming deployment challenges
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Summary
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Chapter 12: Deploying, Testing, and Operating an Intelligent MSA Enterprise System
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Chapter 12: Deploying, Testing, and Operating an Intelligent MSA Enterprise System
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Overcoming system dependencies
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Deploying the MSA system
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Testing and tuning the MSA system
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The post-deployment review
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Summary
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Index
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Index
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Why subscribe?
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Other Books You May Enjoy
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Other Books You May Enjoy
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Packt is searching for authors like you
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Importance of MSA and Machine Learning in Enterprise Systems

In today’s market, the competition has never been fiercer, and user requirements for IT systems are constantly increasing. To be able to keep up with customer requirements and market demands, the need for a shorter time-to-market (TTM) for IT systems has never been more important, all of which has pushed for agile deployment and the need to streamline the development process and leverage as much code reuse as possible.

Microservices architecture (MSA) addresses these concerns and tries to deliver a more competitive, reliable, and rapid deployment and update delivery while maintaining an efficient, stable system operation.

In this chapter, we will learn more details about how microservices help build a modern, flexible, scalable, and resilient enterprise system. The chapter will go over key concepts in MSA and discuss the common enterprise system architectures, how each architecture is different from MSA, why they are different, and what you gain or lose when you adopt one or more architectures over the others.

We will cover the following areas as we go over the chapter:

  • What MSA is and why
  • MSA versus monolithic enterprise systems
  • Service-driven architecture, event-driven architecture (EDA), and how to incorporate that in MSA
  • Challenges of deploying and operating MSA enterprise systems
  • Why it is important to embrace DevOps in building MSA
CONTINUE READING
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Tech Concepts
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Programming languages
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Machine Learning in Microservices
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