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gold devops data science 2024

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Textbook in PDF format 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, DevOps is a set of practices, principles and tools that help software engineers reliably deploy work to production. This book takes the lessons of DevOps 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 DevOps 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. Introduction I DevOps Lessons for Data Science Environments as Code Data Project Architecture Databases and Data APIs Logging and Monitoring Deployments and Code Promotion Demystifying Docker II IT/Admin for Data Science The Cloud The Command Line Linux Administration Application Administration Server Resources and Scaling Computer Networks Domains and DNS SSL/TLS and HTTPS III Enterprise-Grade Data Science Enterprise Networking Auth in Enterprise Compute at Enterprise Scale Package Management in the Enterprise V Appendices

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Gold A. DevOps for Data Science 2024.pdf 16.2 MB

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gold devops data science 2024

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