Book description
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 machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production.
Authors Yaron Haviv and Noah Gift take a production-first approach. Rather than beginning with the ML model, you'll learn how to design a continuous operational pipeline, while making sure that various components and practices can map into it. By automating as many components as possible, and making the process fast and repeatable, your pipeline can scale to match your organization's needs.
You'll learn how to provide rapid business value while answering dynamic MLOps requirements. This book will help you:
- Learn the MLOps process, including its technological and business value
- Build and structure effective MLOps pipelines
- Efficiently scale MLOps across your organization
- Explore common MLOps use cases
- Build MLOps pipelines for hybrid deployments, real-time predictions, and composite AI
- Learn how to prepare for and adapt to the future of MLOps
- Effectively use pre-trained models like HuggingFace and OpenAI to complement your MLOps strategy
Publisher resources
Table of contents
- Preface
- 1. MLOps: What Is It and Why Do We Need It?
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2. The Stages of MLOps
- Getting Started
- Data Collection and Preparation
- Model Development and Training
- Deployment (and Online ML Services)
- Continuous Model and Data Monitoring
- 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
- 3. Getting Started with Your First MLOps Project
- 4. Working with Data and Feature Stores
- 5. Developing Models for Production
- 6. Deployment of Models and AI Applications
- 7. Building a Production Grade MLOps Project from A to Z
- 8. Building Scalable Deep Learning and Large Language Model Projects
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9. Solutions for Advanced Data Types
- ML Problem Framing with Time Series
- Build Versus Buy for MLOps NLP Problems
- Build Versus Buy: The Hugging Face Approach
- Exploring Natural Language Processing with AWS
- Exploring NLP with OpenAI
- Video Analysis, Image Classification, and Generative AI
- Image Classification Techniques with CreateML
- Composite AI
- Conclusion
- Critical Thinking Discussion Questions
- Exercises
- 10. Implementing MLOps Using Rust
- A. Job Interview Questions
- B. Enterprise MLOps Interviews
- Index
- About the Authors
Product information
- Title: Implementing MLOps in the Enterprise
- Author(s):
- Release date: December 2023
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781098136581