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.

本書針對數據科學從業人員，考慮到不同行業之間的定制問題、標準和技術堆棧。它將引導您通過技術決策的基礎知識，包括規劃、構建、優化、打包和部署端到端、可靠且強大的隨機工作流程，使用數據科學的語言。

《MLOps生命周期工具包》將引導您了解軟件工程的原則，假設您沒有先前的經驗。它早期解答了MLOps的永恆問題，並深入了解工程隨機系統的獨特挑戰。接下來，您將發現學習軟件工藝、數據驅動的測試框架和計算機科學的資源。此外，您還將了解如何從Jupyter筆記本過渡到代碼編輯器，並利用基礎設施和雲服務來掌握整個機器學習生命周期。您將獲得有關可能遇到的技術和架構決策的見解，以及部署準確、可擴展、可擴展和可靠模型的最佳實踐。通過實踐實驗，您將構建自己的MLOps“工具包”，可以用於加速自己的項目。在後面的章節中，作者Dayne Sorvisto通過將金融、能源、醫療保健和技術等行業的困難問題作為案例研究，以及塑造決策的道德和技術限制，以深思熟慮的自下而上的方式來考慮機器學習工程。

閱讀本書後，無論您是數據科學家、產品經理還是行業決策者，您都將能夠將模型部署到生產環境中，了解MLOps在您行業的領域語言中的細微差別，並擁有持續交付和學習的資源。

您將學到什麼：
- 理解軟件工程和MLOps的原則
- 設計端到端的機器學習系統
- 平衡技術決策和架構折衷
- 了解每個行業獨特的基本問題以及如何解決它們

本書適合對象：
數據科學家、機器學習工程師和軟件專業人員。

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。