Machine Learning in Microservices: Productionizing microservices architecture for machine learning solutions

Implement real-world machine learning in a microservices architecture as well as design, build, and deploy intelligent microservices systems using examples and case studies

Key Features

• Design, build, and run microservices systems that utilize the full potential of machine learning
• Discover the latest models and techniques for combining microservices and machine learning to create scalable systems
• Implement machine learning in microservices architecture using open source applications with pros and cons

Book Description

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.

What you will learn

• Recognize the importance of MSA and ML and deploy both technologies in enterprise systems
• Explore MSA enterprise systems and their general practical challenges
• Discover how to design and develop microservices architecture
• Understand the different AI algorithms, types, and models and how they can be applied to MSA
• Identify and overcome common MSA deployment challenges using AI and ML algorithms
• Explore general open source and commercial tools commonly used in MSA enterprise systems

Who This Book Is For

This book is for machine learning solution architects, system and machine learning developers, and system and solution integrators of private and public sector organizations. Basic knowledge of DevOps, system architecture, and artificial intelligence (AI) systems is assumed, and working knowledge of the Python programming language is highly desired.

在微服務架構中實現真實世界的機器學習，並使用實例和案例研究設計、構建和部署智能微服務系統。

主要特點：

- 設計、構建和運行充分利用機器學習潛力的微服務系統。
- 探索結合微服務和機器學習的最新模型和技術，創建可擴展的系統。
- 使用開源應用程序以及優點和缺點在微服務架構中實現機器學習。

書籍描述：

隨著對敏捷開發和極短的上市時間的系統部署需求的增加，將機器學習算法納入解耦的細粒度微服務系統中，為現代系統提供了完美的技術組合。《機器學習在微服務中》是您在這個不斷演變的技術世界中保持領先的必備指南。

本書首先介紹了機器學習微服務架構（MSA）的概念，並將MSA與基於服務和事件驅動的架構進行了比較，以及如何過渡到MSA。接下來，您將了解構建MSA的不同方法，並了解如何克服MSA設計中面臨的常見實際挑戰。隨著您的進一步學習，您將深入了解機器學習（ML）概念，並了解它們如何幫助更好地設計和運行MSA系統。最後，本書將通過實際示例和開源應用程序帶您建立和運行高效靈活的微服務系統。

通過閱讀本書，您將清楚了解微服務架構和機器學習的不同模型，並能夠結合這兩種技術，提供靈活且高度可擴展的企業系統。

您將學到：

- 認識MSA和ML的重要性，並在企業系統中部署這兩種技術。
- 探索MSA企業系統及其一般實際挑戰。
- 了解如何設計和開發微服務架構。
- 理解不同的AI算法、類型和模型，以及它們如何應用於MSA。
- 通過使用AI和ML算法識別和克服常見的MSA部署挑戰。
- 探索在MSA企業系統中常用的開源和商業工具。

適合閱讀對象：

本書適合機器學習解決方案架構師、系統和機器學習開發人員，以及私營和公共部門組織的系統和解決方案集成商。假定具有DevOps、系統架構和人工智能（AI）系統的基本知識，並且對Python編程語言有一定的工作知識。

1. Importance of MSA and Machine Learning in Enterprise Systems
2. Breaking Down Your Application and Business Problem into Microservices
3. Solving common MSA Enterprise System Challenges
4. Hands-on Machine Key Machine Learning Algorithms & Examples
5. Machine Learning System Design
6. Stabilizing The Machine Learning System
7. How Machine Learning and Deep Learning help in MSA Enterprise Systems
8. The role of DevOps in Building Intelligent MSA Enterprise Systems
9. Building an MSA with Docker Containers
10. Building your Intelligent MSA Enterprise System
11. Transformation towards Intelligent MSA System – Greenfield vs. Brownfield Deployment
12. Testing, Deploying, and Operating an Intelligent MSA Enterprise System

1. MSA和機器學習在企業系統中的重要性
2. 將應用程序和業務問題拆分為微服務
3. 解決常見的MSA企業系統挑戰
4. 實踐機器學習算法和示例
5. 機器學習系統設計
6. 穩定機器學習系統
7. 機器學習和深度學習在MSA企業系統中的作用
8. DevOps在構建智能MSA企業系統中的角色
9. 使用Docker容器構建MSA
10. 構建智能MSA企業系統
11. 智能MSA系統的轉型-綠地部署與棕地部署
12. 測試、部署和運營智能MSA企業系統