DevOps for Data Science
Sorozatcím: Chapman & Hall/CRC Data Science Series;
-
10% KEDVEZMÉNY?
- A kedvezmény csak az 'Értesítés a kedvenc témákról' hírlevelünk címzettjeinek rendeléseire érvényes.
- Kiadói listaár GBP 64.99
-
32 891 Ft (31 325 Ft + 5% áfa)
Az ár azért becsült, mert a rendelés pillanatában nem lehet pontosan tudni, hogy a beérkezéskor milyen lesz a forint árfolyama az adott termék eredeti devizájához képest. Ha a forint romlana, kissé többet, ha javulna, kissé kevesebbet kell majd fizetnie.
- Kedvezmény(ek) 10% (cc. 3 289 Ft off)
- Discounted price 29 602 Ft (28 193 Ft + 5% áfa)
32 891 Ft
Beszerezhetőség
Becsült beszerzési idő: A Prosperónál jelenleg nincsen raktáron, de a kiadónál igen. Beszerzés kb. 3-5 hét..
A Prosperónál jelenleg nincsen raktáron.
Why don't you give exact delivery time?
A beszerzés időigényét az eddigi tapasztalatokra alapozva adjuk meg. Azért becsült, mert a terméket külföldről hozzuk be, így a kiadó kiszolgálásának pillanatnyi gyorsaságától is függ. A megadottnál gyorsabb és lassabb szállítás is elképzelhető, de mindent megteszünk, hogy Ön a lehető leghamarabb jusson hozzá a termékhez.
A termék adatai:
- Kiadás sorszáma 1
- Kiadó Chapman and Hall
- Megjelenés dátuma 2024. június 19.
- ISBN 9781032100340
- Kötéstípus Keménykötés
- Terjedelem274 oldal
- Méret 234x156 mm
- Súly 670 g
- Nyelv angol
- Illusztrációk 1 Illustrations, black & white; 38 Illustrations, color; 1 Line drawings, black & white; 38 Line drawings, color; 33 Tables, black & white 622
Kategóriák
Rövid leírás:
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.
TöbbHosszú leírás:
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.
Tartalomjegyzék:
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.
Több
