MLOps Lifecycle Toolkit: A Software Engineering Roadmap for Designing, Deploying, and Scaling Stochastic Systems

✍ Scribed by Dayne Sorvisto

• Publisher: AclerPress
• Year: 2023
• Tongue: English
• Leaves: 285
• Category: Library

✦ Synopsis

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.

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.

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.

✦ Table of Contents

Table of contents
About this book
Keywords
Authors and Affiliations
About the author
Bibliographic Information

This is a preview of subscription content, access via your institution.
Table of contents (9 chapters)
Search within book

Front Matter
Pages i-xxii
PDF
Introducing MLOps
    Dayne Sorvisto 
Pages 1-34
Foundations for MLOps Systems
    Dayne Sorvisto 
Pages 35-66
Tools for Data Science Developers
    Dayne Sorvisto 
Pages 67-102
Infrastructure for MLOps
    Dayne Sorvisto 
Pages 103-138
Building Training Pipelines
    Dayne Sorvisto 
Pages 139-165
Building Inference Pipelines
    Dayne Sorvisto 
Pages 167-187
Deploying Stochastic Systems
    Dayne Sorvisto 
Pages 189-216
Data Ethics
    Dayne Sorvisto 
Pages 217-236
Case Studies by Industry
    Dayne Sorvisto 
Pages 237-257
Back Matter