| 000 | 02432cam a2200373 i 4500 | ||
|---|---|---|---|
| 001 | 23049201 | ||
| 005 | 20240310114549.0 | ||
| 008 | 230405t20222022caua f b 001 0 eng d | ||
| 010 | _a 2023275143 | ||
| 020 |
_a9781098107963 _q(paperback) |
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| 035 | _a(OCoLC)on1286145997 | ||
| 035 | _a(Sirsi) i9781098107963 | ||
| 040 |
_aYDX _beng _cYDX _erda _dBDX _dUKMGB _dCDX _dUOK _dOCLCF _dOTP _dDLC |
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| 042 | _alccopycat | ||
| 050 | 0 | 0 |
_aQ325.5 _b.H89 2022 |
| 082 | 0 | 0 | _a006.31 |
| 100 | 1 |
_aHuyen, Chip, _eauthor _93322 |
|
| 245 | 1 | 0 |
_aDesigning machine learning systems : _ban iterative process for production-ready applications _c/ Chip Huyen. |
| 250 | _aFirst edition. | ||
| 264 | 1 |
_aSebastopol, CA : _bO'Reilly Media, Inc., _c2022. |
|
| 264 | 4 | _c©2022 | |
| 300 |
_axvi, 367 pages : _billustrations; _c24 cm |
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| 336 |
_astill image _bsti _2rdacontent |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_aunmediated _bn _2rdamedia |
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| 338 |
_avolume _bnc _2rdacarrier |
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| 504 | _aIncludes bibliographical references and index. | ||
| 505 | 0 | 0 |
_tOverview of machine learning systems -- _tIntroduction to machine learning systems design -- _tData engineering fundamentals -- _tTraining data -- _tFeature Engineering -- _tModel development and offline evaluation -- _tModel develoypment and prediction service -- _tData distribution shifts and monitoring -- _tContinual learning and test in production -- _tInfrastructure and tooling for MLOps -- _tThe human side of machine learning |
| 520 | _a"Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references."--Amazon.com. | ||
| 596 | _a1 3 | ||
| 598 | _aNEWBOOKS | ||
| 650 | 0 |
_aApplication software _xDesign _93323 |
|
| 650 | 0 |
_aMachine learning. _92745 |
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| 999 |
_c1012 _d1012 |
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