Название: Production Engineering from DevOps to MLOps
Автор: Arnab Bose, Sebastien Donadio
Издательство: Leanpub
Год: 2023-06-29
Страниц: 124
Язык: английский
Формат: pdf (true), epub
Размер: 10.2 MB

The book to bridge DevOps and MLOps.

This book takes a DevOps approach to MLOps and uniquely positions how MLOps is an extension of well-established DevOps principles using real-world use cases. It leverages multiple DevOps concepts and methodologies such as CI/CD and software testing. It also demonstrates the additional concepts from MLOps such as continuous training that expands CI/CD/CT to build, operationalize and monitor ML models.

Who this book is for:
This book targets three different personas. First, data engineers and DevOps engineers who manage ML data and model platforms, deploy ML model software into production and monitor them. Second, full-stack data scientists who not only build ML models but work on the end-to-end stack of the ML lifecycle starting with data ingestion to production deployment and monitoring. Third, project managers who need to understand the intricacies of the different steps in taking an ML model to production.

Book structure:
This book takes a DevOps approach to MLOps and uniquely positions how MLOps is an extension of well-established DevOps principles using real-world use cases. It leverages multiple DevOps concepts and methodologies such as CI/CD and software testing. It also demonstrates the additional concepts from MLOps such as continuous training that expands CI/CD/CT to build, operationalize and monitor ML models.

We lead the readers to build a full DevOps/ML infrastructure by using a collection of real-world case studies. It goes into the details of the principles starting from DevOps to the domain of MLOps which focuses on operationalizing and monitoring ML models.

What this book covers:
Chapter 1: Introducing DevOps , gives an overview of what DevOps is. It will introduce what an operating system is and how we can operate it.
Chapter 2: Understanding clouding for devops , introduces how we can use the cloud in the DevOps approach.
Chapter 3: Building software by understanding the whole toolchain , covers how to build software and how to enrich it with libraries.
Chapter 4: Introducing MLOps, gets an insight into the challenges in operationalizing Machine Learning models.
Chapter 5: Preparing the Data , gives insight into the importance of data in Machine Learning models.
Chapter 6: Using a Feature Store , explains how a feature store promotes reusability to develop quick robust Machine Learning models.
Chapter 7: Building ML models , describes how to manage a Machine Learning model.
Chapter 8 Understanding ML Pipelines , depicts the importance of data feedback loops.
Chapter 9. Interpreting and Explaining ML models , discusses how to dissect an ML model from the perspectives of explainability and interpretability.
Chapter 10 Building Containers and Managing Orchestration , presents the use of containerized applications.
Chapter 11 Testing ML Models , describes how testing improves software quality; it is a critical part of the DevOps/MLOps process.
Chapter 12 Monitoring ML Models , illustrates what are the different ways that an ML model can underperform in production.
Chapter 13 Evaluating Fairness , explain what bias and fairness are in ML models.
Chapter 14 Exploring Antifragility and ML Model Environmental Impact , comprehends antifragility and how it can be used to make your ML model robust and the environment impact of your ML model.