DevOps for Data Science

Data Scientists are experts at analyzing modelling and visualizing data but at one point or another have all encountered difficulties in collaborating with or delivering their work to the people and systems that matter. Born out of the agile software movement Dev Ops is a set of practices principles and tools that help software engineers reliably deploy work to production. This book takes the lessons of Dev Ops and aplies them to creating and delivering production-grade data science projects in Python and R. This book’s first section explores how to build data science projects that deploy to production with no frills or fuss. Its second section covers the rudiments of administering a server including Linux application and network administration before concluding with a demystification of the concerns of enterprise IT/Administration in its final section making it possible for data scientists to communicate and collaborate with their organization’s security networking and administration teams. Key Features:• Start-to-finish labs take readers through creating projects that meet Dev Ops best practices and creating a server-based environment to work on and deploy them. • Provides an appendix of cheatsheets so that readers will never be without the reference they need to remember a Git Docker or Command Line command. • Distills what a data scientist needs to know about Docker APIs CI/CD Linux DNS SSL HTTP Auth and more. • Written specifically to address the concern of a data scientist who wants to take their Python or R work to production. There are countless books on creating data science work that is correct. This book on the otherhand aims to go beyond this targeted at data scientists who want their work to be than merely accurate and deliver work that matters. | DevOps for Data Science