DevOps for Data Science
Series: Chapman & Hall/CRC Data Science Series;
- Publisher's listprice GBP 64.99
-
32 891 Ft (31 325 Ft + 5% VAT)
The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.
- Discount 10% (cc. 3 289 Ft off)
- Discounted price 29 602 Ft (28 193 Ft + 5% VAT)
32 891 Ft
Availability
Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
Not in stock at Prospero.
Why don't you give exact delivery time?
Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Edition number 1
- Publisher Chapman and Hall
- Date of Publication 19 June 2024
- ISBN 9781032100340
- Binding Hardback
- No. of pages274 pages
- Size 234x156 mm
- Weight 670 g
- Language English
- Illustrations 1 Illustrations, black & white; 38 Illustrations, color; 1 Line drawings, black & white; 38 Line drawings, color; 33 Tables, black & white 622
Categories
Short description:
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.
MoreLong description:
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.
Table of Contents:
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.
More
