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

The book MLOps Lifecycle Toolkit is for practitioners of data science, with consideration for bespoke problems, standards, and tech stacks between industries. It guides 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.

Format: Paperback / softback
Length: 190 pages
Publication date: 15 August 2023
Publisher: APress

This comprehensive book is meticulously crafted to cater to the needs of data science practitioners, taking into account the diverse requirements, standards, and technology stacks across industries. It serves as a valuable guide for navigating the intricacies of technical decision-making, encompassing a wide range of topics such as planning, building, optimizing, packaging, and deploying end-to-end, reliable, and robust stochastic workflows using the language of data science.

MLOps Lifecycle Toolkit takes a unique approach by assuming no prior experience in software engineering. It delves into the fundamental principles of software engineering, addressing the core "why" of MLOps early on. Additionally, the book provides valuable insights into the unique challenges associated with engineering stochastic systems.

To enhance your skills, the book offers a wealth of resources to learn software craftsmanship, data-driven testing frameworks, and computer science. It also demonstrates how to transition from Jupyter notebooks to code editors, effectively leveraging infrastructure and cloud services to take control of the entire machine learning lifecycle. By exploring the technical and architectural decisions involved, as well as best practices for deploying accurate, extensible, scalable, and reliable models, readers will gain a deep understanding of the MLOps landscape.

Through hands-on labs, readers will have the opportunity to build their own MLOps "toolkit," which they can utilize to accelerate their projects. In later chapters, author Dayne Sorvisto adopts a thoughtful and bottom-up approach to machine learning engineering, examining the hard problems specific to industries like high finance, energy, healthcare, and technology through case studies. Additionally, the book explores the ethical and technical constraints that shape decision-making in these domains.

By the end of this book, whether you are a data scientist, product manager, or industry decision-maker, you will possess the necessary skills and resources to deploy models to production, effectively communicate the nuances of MLOps within the language of your industry, and establish a continuous delivery and learning culture. MLOps Lifecycle Toolkit is a must-read for anyone seeking to excel in the field of machine learning operations and drive innovation in their organizations.

Weight: 458g
Dimension: 155 x 235 x 20 (mm)
ISBN-13: 9781484296417
Edition number: 1st ed.