MARC details
| 000 -LEADER |
|---|
| fixed length control field |
01859nam a22003017a 4500 |
| 001 - CONTROL NUMBER |
|---|
| control field |
22537255 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
IIITD |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20240504164813.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
220506t20202021caua 001 0 eng d |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER |
|---|
| LC control number |
2021443780 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9789385889219 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
IIITD |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.31 |
| Item number |
LAK-M |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Lakshmanan, Valliappa |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Machine learning design patterns : |
| Remainder of title |
solutions to common challenges in data preparation, model building, and MLOps |
| Statement of responsibility, etc |
by Valliappa Lakshmanan, Sara Robinson and Michael Munn |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Beijng : |
| Name of publisher, distributor, etc |
O'Reilly, |
| Date of publication, distribution, etc |
©2022 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xiv, 390 p. : |
| Other physical details |
ill. ; |
| Dimensions |
23 cm. |
| 501 ## - WITH NOTE |
|---|
| With note |
Includes index. |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Title |
The need for machine learning design patterns -- Data representation design patterns -- Problem representation design patterns -- Model training patterns -- Design patterns for resilient serving -- Reproducibility design patterns -- Responsible AI -- Connected patterns. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation.-- |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine learning. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Big data. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Big data. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine learning. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Robinson, Sara |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Munn, Michael |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Books |
