Название: MLOps Lifecycle Toolkit: A Software Engineering Roadmap for Designing, Deploying, and Scaling Stochastic SystemsАвтор: Dayne Sorvisto
Издательство: Apress
Год: 2023
Страниц: 285
Язык: английский
Формат: pdf (true), epub
Размер: 10.2 MB
This book is aimed at practitioners of data science, with consideration for bespoke problems, standards, and tech stacks between industries. It will guide you through the fundamentals of technical decision making, including planning, building, optimizing, packaging, and deploying end-to-end, reliable, and robust stochastic workflows using the language of Data Science.
MLOps Lifecycle Toolkit walks you through the principles of software engineering, assuming no prior experience. It addresses the perennial “why” of MLOps early, along with insight into the unique challenges of engineering stochastic systems. Next, you’ll discover resources to learn software craftsmanship, data-driven testing frameworks, and computer science. Additionally, you will see how to transition from Jupyter notebooks to code editors, and leverage infrastructure and cloud services to take control of the entire Machine Learning lifecycle. You’ll gain insight into the technical and architectural decisions you’re likely to encounter, as well as best practices for deploying accurate, extensible, scalable, and reliable models. Through hands-on labs, you will build your own MLOps “toolkit” that you can use to accelerate your own projects. In later chapters, author Dayne Sorvisto takes a thoughtful, bottom-up approach to machine learning engineering by considering the hard problems unique to industries such as high finance, energy, healthcare, and tech as case studies, along with the ethical and technical constraints that shape decision making.
In this book you will build your own MLOps toolkit that you can use in your own projects, develop intuition, and understand MLOps at a conceptual level. The software toolkit is developed throughout the book with each chapter adding tools that map to different phases of the MLOps lifecycle from model training, model inference and deployment to data ethics. With plenty of industry examples along the way from finance to energy and healthcare, this book will help you make data-driven technical decisions, take control of your own model artifacts, and accelerate your technical roadmap.
After reading this book, whether you are a data scientist, product manager, or industry decision maker, you will be equipped to deploy models to production, understand the nuances of MLOps in the domain language of your industry, and have the resources for continuous delivery and learning.
All source code used in this book can be downloaded from Github.
What You Will Learn:
Understand the principles of software engineering and MLOps
Design an end-to-end machine learning system
Balance technical decisions and architectural trade-offs
Gain insight into the fundamental problems unique to each industry and how to solve them
Who This Book Is For:
Data scientists, Machine Learning engineers, and software professionals.
Contents:
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