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...
| Main Authors: | , |
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| Format: | eBook |
| Language: | English |
| Published: |
Sebastopol, CA
O'Reilly Media, Inc.
2023
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| Subjects: | |
| Online Access: | |
| Collection: | O'Reilly - Collection details see MPG.ReNa |
Table of Contents:
- 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
- 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
- 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
- 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
- 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