Machine Learning in Microservices : Productionizing Microservices Architecture for Machine Learning Solutions

Machine Learning in Microservices Productionizing Microservices Architecture for Machine Learning Solutions

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 dep...

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Detalles Bibliográficos
Autor principal: Abouahmed, Mohamed (-)
Otros Autores: Ahmed, Omar
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham : Packt Publishing, Limited 2023.
Edición:1st ed
Materias:
Service-oriented architecture (Computer science)
Machine learning.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009729738806719
Tabla de Contenidos:
  • 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

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