MLOps Lifecycle Toolkit by Dayne Sorvisto (.ePUB)+
File Size: 10 MB
MLOps Lifecycle Toolkit: A Software Engineering Roadmap for Designing, Deploying, and Scaling Stochastic Systems by Dayne Sorvisto
Requirements: .ePUB, .PDF reader, 10 MB | True PDF, True EPUB
Overview: 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.
Genre: Non-Fiction > Tech & Devices
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