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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 u...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[S.l.] :
PACKT PUBLISHING LIMITED,
2023.
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| Edición: | 1st edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Abouahmed, Mohamed, |e author. | |
| 245 | 1 | 0 | |a MACHINE LEARNING IN MICROSERVICES |h [electronic resource] : |b productionizing microservices architecture for machine learning solutions / |c Mohamed Abouahmed, Omar Ahmed. |
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| 520 | |a 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. | ||
| 505 | 0 | |a 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) | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
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