Implementing MLOps in the Enterprise

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
• Build production applications with LLMs and Generative AI, while reducing risks, increasing the efficiency, and fine tuning models
• 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

Table of Contents

Chapter 1. M LOps: What Is It and Why Do We Need It?

Chapter 2. The Stages of MLOps

Chapter 3. Getting Started with Your First MLOps Project

Chapter 4. Working with Data and Feature Stores

Chapter 5. Developing Models for Production

Chapter 6. Deployment of Models and Al Applications

Chapter 7. Building a Production Grade MLOps Project from A to Z

Chapter 8. Building Scalable Deep Learning and Large Language Model Projects

Chapter 9. Solutions for Advanced Data Types

Chapter 10. Implementing MLOps Using Rust

Appendix A. Job Interview Questions

Appendix B. Enterprise MLOps Interviews

About the Authors

Yaron Haviv is a serial entrepreneur who has been applying his deep technological experience in data, cloud, AI and networking to leading startups and enterprise companies since the late 1990s. As the co-founder and CTO of Iguazio, Yaron drives the strategy for the company’s data science platform and leads the shift towards real- time AI. He also initiated and built Nuclio, a leading open source serverless platform with over 4,000 Github stars and MLRun, Iguazio’s open source MLOps orchestration framework.

Noah Gift is the founder of Pragmatic A.I. Labs. Noah Gift lectures at MSDS, at Northwestern, Duke MIDS Graduate Data Science Program, the Graduate Data Science program at UC Berkeley, the UC Davis Graduate School of Management MSBA program, UNC Charlotte Data Science Initiative and University of Tennessee (as part of the Tennessee Digital Jobs Factory). He teaches and designs graduate machine learning, MLOps, A.I., Data Science courses, and consulting on Machine Learning and Cloud Architecture for students and faculty. These responsibilities include leading a multi-cloud certification initiative for students.