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 Importance of MSA and Machine Learning in Enterprise Systems Refactoring Your Monolith Solving Common MSA Enterprise System Challenges Key Machine Learning Algorithms and Concepts Machine Learning System Design Stabilizing the Machine Learning System How Machine Learning and Deep Learning Help in MSA Enterprise Systems The Role of DevOps in Building Intelligent MSA Enterprise Systems Building an MSA with Docker Containers Building an Intelligent MSA Enterprise System Managing the New System's Deployment – Greenfield versus Brownfield Deploying, Testing, and Operating an Intelligent MSA Enterprise System Cover Title Page Copyright and Credits Dedication Contributors Table of Contents Preface Part 1: Overview of Microservices Design and Architecture Chapter 1: Importance of MSA and Machine Learning in Enterprise Systems Why microservices? Pros and cons Advantages of microservices Disadvantages of microservices The benefits outweigh the detriments Loosely versus tightly coupled monolithic systems Service-driven, EDA, and MSA hybrid model architecture ACID transactions Saga patterns Command Query Responsibility Segregation (CQRS) DevOps in MSA Why ML? Summary Chapter 2: Refactoring Your Monolith Identifying the system’s microservices The ABC monolith The ABC-Monolith’s current functions The ABC-Monolith’s database The ABC workflow and current function calls Function decomposition Data decomposition Request decomposition Summary Chapter 3: Solving Common MSA Enterprise System Challenges MSA isolation using an ACL Using an API gateway Service catalogs and orchestrators Microservices aggregators Gateways versus orchestrators versus aggregators Microservices circuit breaker ABC-MSA enhancements Summary Part 2: Overview of Machine Learning Algorithms and Applications Chapter 4: Key Machine Learning Algorithms and Concepts The differences between artificial intelligence, machine learning, and deep learning Common deep learning and machine learning libraries used in Python Building regression models Building multiclass classification Text sentiment analysis and topic modeling Pattern analysis and forecasting in machine learning Enhancing models using deep learning Summary Chapter 5: Machine Learning System Design Machine learning system components Fit and transform interfaces Transform Fit Train and serve interfaces Training Serving Orchestration Summary Chapter 6: Stabilizing the Machine Learning System Machine learning parameterization and dataset shifts The causes of dataset shifts Identifying dataset shifts Handling and stabilizing dataset shifts Summary Chapter 7: How Machine Learning and Deep Learning Help in MSA Enterprise Systems Machine learning MSA enterprise system use cases Enhancing system supportability and time-to-resolution (TTR) with pattern analysis machine learning Implementing system self-healing with deep learning Summary Part 3: Practical Guide to Deploying Machine Learning in MSA Systems Chapter 8: The Role of DevOps in Building Intelligent MSA Enterprise Systems DevOps and organizational structure alignment DevOps The DevOps team structure DevOps processes in enterprise MSA system operations The Agile methodology of development Automation Applying DevOps from the start to operations and maintenance Source code version control Configuration management and everything as a code CI/CD Code quality assurance Monitoring Disaster management Summary Chapter 9: Building an MSA with Docker Containers What are containers anyway, and why use them? Installing Docker Docker Engine installation Docker components Creating ABC-MSA containers ABC-MSA containers Managing your system’s containers ABC-MSA microservice inter-communication The Docker network TCP/IP communication between containers/microservices Summary Chapter 10: Building an Intelligent MSA Enterprise System The machine learning advantage Building your first AI microservice The anatomy of AI enhancements The self-healing process Building the necessary tools The intelligent MSA system in action Initializing the ABC-Intelligent-MSA system Building and using the training data Simulating the ABC-Intelligent-MSA’s operation Analyzing AI service operations The PBW in action The PAD in action Summary Chapter 11: Managing the New System’s Deployment – Greenfield versus Brownfield Deployment strategies Greenfield versus brownfield deployment Flexibility Scalability Technology stack Integration Cost Time-to-market Risks Staff onboarding User adoption Overcoming deployment challenges Identify deployment risks Prioritize risks Developing and implementing a risk mitigation plan The rollback plan Test, monitor, and adjust Post-deployment and pre-production review Summary Chapter 12: Deploying, Testing, and Operating an Intelligent MSA Enterprise System Overcoming system dependencies Reusable ABC-Monolith components and dependencies Mitigating ABC-Intelligent-MSA deployment risks Deploying the MSA system The anti-corruption layer Integrating the MSA system’s services Testing and tuning the MSA system The post-deployment review Checking the new system’s performance Identifying and fixing system defects Compliance System maintenance and updates User satisfaction Summary Index About Packt Other Books You May Enjoy Agile development and quick time-to-market deployments are crucial for competitive markets and dynamic needs, and deploying artificial intelligence technologies in microservices architecture creates flexible and adaptive systems. This practical guide helps developers and architects to design and deploy intelligent microservices systems.