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.
Dayne Sorvisto has a Master of Science degree in Mathematics and Statistics and became an expert in MLOps. He started his career in data science before becoming a software engineer. He has worked for tech start-ups and has consulted for Fortune 500 companies in diverse industries including energy and finance. Dayne has previously won awards for his research including Industry Track Best Paper Award. Dayne has also written about security in MLOps systems for Dell EMC's Proven Professional Knowledge Sharing platform and has contributed to many of the open-source projects he uses regularly.
Dayne Sorvisto擁有數學和統計學的碩士學位，並成為MLOps的專家。他在成為軟體工程師之前從事數據科學的職業生涯。他曾在科技初創公司工作，並為包括能源和金融在內的財富500強公司提供諮詢服務。Dayne以前曾因其研究成果獲得獎項，包括行業軌道最佳論文獎。Dayne還在Dell EMC的Proven Professional Knowledge Sharing平台上撰寫了有關MLOps系統安全性的文章，並經常為他使用的許多開源項目做出貢獻。