Machine Learning in Microservices: Productionizing microservices architecture for machine learning solutions. Este artículo no está disponible.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

Purchase of the print or Kindle book includes a free PDF eBook

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.

Table of Contents

1. Importance of MSA and Machine Learning in Enterprise Systems
2. Refactoring Your Monolith
3. Solving Common MSA Enterprise System Challenges
4. Key Machine Learning Algorithms and Concepts
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 an Intelligent MSA Enterprise System
11. Managing the New System’s Deployment – Greenfield versus Brownfield
12. Deploying, Testing, and Operating an Intelligent MSA Enterprise System

Mohamed Abouahmed is a Principal Architect and Consultant providing a unique combination of technical and commercial expertise to resolve complex and business-critical issues through the design and delivery of innovative, technology-driven systems and solutions. He is specialized in network automation solutions and smart systems development and deployment.

Mohamed’s hands-on experience underpinned by his strong project management and academic background, which includes a PMP Certification, Master of Global Management, Master of Business Administration (International Business), Master of Science in Computer Networking, and BSc in Electronics Engineering, helped him develop and deliver robust solutions for multiple carriers, service providers, enterprises, and Fortune 200 clients.

Omar Ahmed is an experienced Computer Engineer with a demonstrated history of working in the information technology and IoT industry. Skilled in C/C++, Databases, Java, Management, and Microcontrollers. Strong engineering professional with a Bachelor's degree focused in Computer Engineering from Georgia Institute of Technology.