DevOps for Data Science
Chapman & Hall/CRC (Verlag)
978-1-032-10034-0 (ISBN)
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.
Alex leads the Solutions Engineering team at Posit (formerly RStudio). In that role, he has advised hundreds of organizations of all sizes and levels of sophistication to create production-grade open-source data science environments. Before coming to Posit, he was a data scientist and data science team lead and worked on politics, consulting, and healthcare.
What is the role of Data Science in orgs? What is the open data science platform: A centralized location to manage data science dev environment, as well as test and deploy content to end-users. Components. Determining requirements for your platform. IT Basics for Data Scientists. Servers and the Cloud. Networking. Security. Logging into different services. Scaling, Common Hardware Configurations. Platform Architecture and Management. Environments. Data Storage and Access. Package Management. Scaling. Appendix.
| Erscheinungsdatum | 10.07.2024 |
|---|---|
| Reihe/Serie | Chapman & Hall/CRC Data Science Series |
| Zusatzinfo | 33 Tables, black and white; 38 Line drawings, color; 1 Line drawings, black and white; 38 Illustrations, color; 1 Illustrations, black and white |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Gewicht | 670 g |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Mathematik / Informatik ► Informatik ► Software Entwicklung | |
| Mathematik / Informatik ► Informatik ► Theorie / Studium | |
| ISBN-10 | 1-032-10034-6 / 1032100346 |
| ISBN-13 | 978-1-032-10034-0 / 9781032100340 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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