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...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Abouahmed, Mohamed (Autor), Ahmed, Omar (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: [S.l.] : PACKT PUBLISHING LIMITED, 2023.
Edición:1st edition.
Temas:
Service-oriented architecture (Computer science)
Machine learning.
Architecture orientée service (Informatique)
Apprentissage automatique.
Machine learning
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
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

Ejemplares similares