| 000 | 01723nam a22002897i 4500 | ||
|---|---|---|---|
| 005 | 20250819135114.0 | ||
| 008 | 250807s2020 ii a|||fr|||| 00| 0 eng d | ||
| 020 | _a9789355510235 | ||
| 040 |
_aEG-GaU _cEG-GaU _dEG-GaU _erda |
||
| 082 | 0 | 4 |
_223 _a005.4 _bP.V.O |
| 100 | 1 |
_aPandey, Vishwajyoti, _eauthor. _961820 |
|
| 245 | 1 | 0 |
_aOperationalizing Machine Learning Pipelines : _bBuilding Reusable and Reproducible Machine Learning Pipelines Using MLOps / _cVishwajyoti Pandey & Shaleen Bengani. |
| 250 | _afirst edition. | ||
| 264 | 1 |
_aNew Delhi : _bBPB Publications, _c2022. |
|
| 300 |
_a162 pages : _billustrations ; _c23 cm. |
||
| 336 |
_2rdacontent _atext _btxt |
||
| 337 |
_2rdamedia _aunmediated _bn |
||
| 338 |
_2rdacarrier _avolume _bnc |
||
| 504 | _aIncludes practical case studies, tool examples, and key feature highlights. | ||
| 505 | _aDS/ML Projects – Initial Setup; ML Projects Lifecycle; ML Architecture – Framework & Components; Data Exploration & Problem Quantification; Training & Testing ML Models; Model Performance Measurement; CRUD with JS Frameworks; Feature Store; Building ML Pipeline. | ||
| 520 | _aA practitioner’s guide to implementing end-to-end MLOps workflows. Covers features such as GitOps automation, feature store creation, serverless pipelines, model serving with KFServing and Polyaxon, and model monitoring in production—ideal for ML engineers, data scientists, and DevOps professionals.turn0search3turn0search12 | ||
| 650 | 0 |
_aMachine learning _ximplementation. _960427 |
|
| 650 | 0 |
_aData engineering _xpipelines. _961821 |
|
| 700 | 1 |
_aBengani, Shaleen, _eauthor. _961822 |
|
| 942 |
_2ddc _cBK |
||
| 999 |
_c11588 _d11588 |
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