Machine Learning Platform Engineering

Delivering a successful machine learning project is hard. This book makes it easier. In it, you’ll design a reliable ML system from the ground up, incorporating MLOps and 15.580along with a stack of proven infrastructure tools including Kubeflow, MLFlow, BentoML, Evidently, and Feast.

A properly designed machine learning system streamlines data workflows, improves collaboration between data and operations teams, and provides much-needed structure for both training and deployment. In this book you’ll learn how to design and implement a machine learning system from the ground up. You’ll appreciate this instantly-useful introduction to achieving the full benefits of automated ML infrastructure.

In Machine Learning Platform Engineering you’ll learn how to:

• Set up an MLOps platform
• Deploy machine learning models to production
• Build end-to-end data pipelines
• Effective monitoring and explainability

About the technology

AI and ML systems have a lot of moving parts, from language libraries and application frameworks, to workflow and deployment infrastructure, to LLMs and other advanced models. A well-designed internal development platform (IDP) gives developers a defined set of tools and guidelines that accelerate the dev process, improving consistency, security, and developer experience.

About the book

Machine Learning Platform Engineering shows you how to build an effective IDP for ML and AI applications. Each chapter illuminates a vital part of the ML workflow, including setting up orchestration pipelines, selecting models, allocating resources for training, inference, and serving, and more. As you go, you’ll create a versatile modern platform using open source tools like Kubeflow, MLFlow, BentoML, Evidently, Feast, and LangChain.

What's inside

• Set up an end-to-end MLOps/LLMOps platform
• Deploy ML and AI models to production
• Effective monitoring, evaluation, and explainability

About the reader

For data scientists or software engineers. Examples in Python.

Table of Contents

Part 1
1 Getting started with MLOps and ML engineering
2 What is MLOps?
3 Building applications on Kubernetes
Part 2
4 Designing reliable ML systems
5 Orchestrating ML pipelines
6 Productionizing ML models
Part 3
7 Data analysis and preparation
8 Model training and validation: Part 1
9 Model training and validation: Part 2
10 Model inference and serving
11 Monitoring and explainability
Part 4
12 Designing LLM-powered systems
13 Production LLM system design
A Installation and setup
B Basics of YAML

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