Introducing MLOps : (Record no. 80342)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01749nam a22001577a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220825b2020 |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789385889769 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 TRE-M |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Treveil, Mark |
| 245 ## - TITLE STATEMENT | |
| Title | Introducing MLOps : |
| Remainder of title | how to scale machine learning in the enterprise / |
| Statement of responsibility, etc. | Mark Treveil |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc. | India |
| Name of publisher, distributor, etc. | Shroff Publishers |
| Date of publication, distribution, etc. | 2020 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 169 p. |
| 365 ## - TRADE PRICE | |
| Price type code | INR |
| Price amount | 750.00. |
| 500 ## - GENERAL NOTE | |
| General note | More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact.<br/><br/>This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout.<br/><br/>This book helps you:<br/><br/>Fulfill data science value by reducing friction throughout ML pipelines and workflows<br/>Refine ML models through retraining, periodic tuning, and complete remodeling to ensure long-term accuracy<br/>Design the MLOps life cycle to minimize organizational risks with models that are unbiased, fair, and explainable<br/>Operationalize ML models for pipeline deployment and for external business systems that are more complex and less standardized. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine learning |
| 952 ## - LOCATION AND ITEM INFORMATION (KOHA) | |
| Withdrawn status | |
| Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Current library | Shelving location | Date acquired | Full call number | Barcode | Date last seen | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | 003-007 | BITS Pilani Hyderabad | General Stack (For lending) | 25/08/2022 | 006.31 TRE-M | 46280 | 13/11/2024 | Books |