| 000 | 00979nam a2200217Ia 4500 | ||
|---|---|---|---|
| 008 | 240825s9999 xx 000 0 und d | ||
| 020 |
_a9789355420374 _qpbk |
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| 041 | _aeng | ||
| 082 |
_a006.31 _bGIF |
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| 100 | _aGift, Noah | ||
| 245 | 0 |
_aPractical MLOps _c/ Noah Gift _boperationalizing machine learning models |
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| 250 | _a1st ed. | ||
| 260 |
_cc2021 _aSebastapol, CA _bO'Reilly Media |
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| 300 |
_axvii, 439 p. _bill. _c23 cm. |
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| 500 | _aReprint, 2021 | ||
| 504 | _aindex | ||
| 520 | _aGetting 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. | ||
| 650 | _a Apprentissage automatique; Machine learning | ||
| 700 | _aAlfredo Deza | ||
| 942 | _cENGLISH | ||
| 999 |
_c541042 _d541042 |
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