Practical MLOps : (Record no. 80344)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01693nam a22001697a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220825b2021 |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789355420374 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 GIF-N |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Gift, Noah |
| 245 ## - TITLE STATEMENT | |
| Title | Practical MLOps : |
| Remainder of title | operatonalizing machine learning models / |
| Statement of responsibility, etc. | Noah Gift and Alfredo Deza |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc. | India |
| Name of publisher, distributor, etc. | Shroff Publishers |
| Date of publication, distribution, etc. | 2021 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 439 p. |
| 365 ## - TRADE PRICE | |
| Price type code | INR |
| Price amount | 1700.00. |
| 500 ## - GENERAL NOTE | |
| General note | Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models.<br/><br/>Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start.<br/><br/>You'll discover how to:<br/><br/>Apply DevOps best practices to machine learning<br/>Build production machine learning systems and maintain them<br/>Monitor, instrument, load-test, and operationalize machine learning systems<br/>Choose the correct MLOps tools for a given machine learning task<br/>Run machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine learning |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Deza, Alfredo |
| 952 ## - LOCATION AND ITEM INFORMATION (KOHA) | |
| Withdrawn status | |
| Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Full call number | Barcode | Date last seen | Date last checked out | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | 003-007 | BITS Pilani Hyderabad | BITS Pilani Hyderabad | General Stack (For lending) | 25/08/2022 | 1 | 006.31 GIF-N | 46278 | 23/04/2025 | 25/03/2025 | 25/08/2022 | Books |