Implementing MLOps in the Enterprise : a production-first approach

Implementing MLOps in the Enterprise a production-first approach

With demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine learning pipelines. This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and ma...

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Detalles Bibliográficos
Otros Autores: Haviv, Yaron, author (author), Gift, Noah, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Sebastopol, CA : O'Reilly Media, Inc 2023.
Materias:
Machine learning.
Information technology > Management.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009785400006719
Tabla de Contenidos:
  • Cover
  • Copyright
  • Table of Contents
  • Preface
  • Who This Book Is For
  • Navigating This Book
  • Conventions Used in This Book
  • Using Code Examples
  • O'Reilly Online Learning
  • How to Contact Us
  • Acknowledgments
  • Yaron
  • Noah
  • Chapter 1. MLOps: What Is It and Why Do We Need It?
  • What Is MLOps?
  • MLOps in the Enterprise
  • Understanding ROI in Enterprise Solutions
  • Understanding Risk and Uncertainty in the Enterprise
  • MLOps Versus DevOps
  • What Isn't MLOps?
  • Mainstream Definitions of MLOps
  • What Is ML Engineering?
  • MLOps and Business Incentives
  • MLOps in the Cloud
  • Key Cloud Development Environments
  • The Key Players in Cloud Computing
  • MLOps On-Premises
  • MLOps in Hybrid Environments
  • Enterprise MLOps Strategy
  • Conclusion
  • Critical Thinking Discussion Questions
  • Exercises
  • Chapter 2. The Stages of MLOps
  • Getting Started
  • Choose Your Algorithm
  • Design Your Pipelines
  • Data Collection and Preparation
  • Data Storage and Ingestion
  • Data Exploration and Preparation
  • Data Labeling
  • Feature Stores
  • Model Development and Training
  • Writing and Maintaining Production ML Code
  • Tracking and Comparing Experiment Results
  • Distributed Training and Hyperparameter Optimization
  • Building and Testing Models for Production
  • Deployment (and Online ML Services)
  • From Model Endpoints to Application Pipelines
  • Online Data Preparation
  • Continuous Model and Data Monitoring
  • Monitoring Data and Concept Drift
  • Monitoring Model Performance and Accuracy
  • The Strategy of Pretrained Models
  • Building an End-to-End Hugging Face Application
  • Flow Automation (CI/CD for ML)
  • Conclusion
  • Critical Thinking Discussion Questions
  • Exercises
  • Chapter 3. Getting Started with Your First MLOps Project
  • Identifying the Business Use Case and Goals
  • Finding the AI Use Case
  • Defining Goals and Evaluating the ROI
  • How to Build a Successful ML Project
  • Approving and Prototyping the Project
  • Scaling and Productizing Projects
  • Project Structure and Lifecycle
  • ML Project Example from A to Z
  • Exploratory Data Analysis
  • Data and Model Pipeline Development
  • Application Pipeline Development
  • Scaling and Productizing the Project
  • CI/CD and Continuous Operations
  • Conclusion
  • Critical Thinking Discussion Questions
  • Exercises
  • Chapter 4. Working with Data and Feature Stores
  • Data Versioning and Lineage
  • How It Works
  • Common ML Data Versioning Tools
  • Data Preparation and Analysis at Scale
  • Structured and Unstructured Data Transformations
  • Distributed Data Processing Architectures
  • Interactive Data Processing
  • Batch Data Processing
  • Stream Processing
  • Stream Processing Frameworks
  • Feature Stores
  • Feature Store Architecture and Usage
  • Ingestion and Transformation Service
  • Feature Storage
  • Feature Retrieval (for Training and Serving)
  • Feature Stores Solutions and Usage Example

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