Description
Book SynopsisThis 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 Toolkitwalks 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 likel
Table of ContentsChapter 1: Introduction to Machine Learning Engineering.- Chapter 2: Developing Stochastic Systems.- Chapter 3: Tools for Data Science Developers.- Chapter 4: Infrastructure for MLOps.- Chapter 5, Building Training Pipelines.- Chapter 6: Building Inference Pipelines.- Chapter 7: Deploying Stochastic Systems.- Chapter 8: Data Ethics.- Chapter 9: Case Studies By Industry.
