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