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

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 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