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| 001 | 979-8-8688-0376-5 | ||
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_a9798868803765 _9979-8-8688-0376-5 |
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_a10.1007/979-8-8688-0376-5 _2doi |
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| 100 | 1 |
_aLuu, Hien. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _978812 |
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| 245 | 1 | 0 |
_aMLOps with Ray _h[electronic resource] : _bBest Practices and Strategies for Adopting Machine Learning Operations / _cby Hien Luu, Max Pumperla, Zhe Zhang. |
| 250 | _a1st ed. 2024. | ||
| 264 | 1 |
_aBerkeley, CA : _bApress : _bImprint: Apress, _c2024. |
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| 300 |
_aXI, 338 p. 111 illus. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 505 | 0 | _aChapter 1: Introduction to MLOps -- Chapter 2: MLOps Adoption Strategy and Case Studies -- Chapter 3: Feature Engineering Infrastructure -- Chapter 4: Model Training Infrastructure -- Chapter 5: Model Serving -- Chapter 6: Machine Learning Observability -- Chapter 7: Ray Core -- Chapter 8: Ray Air -- Chapter 9: The Future of MLOps. | |
| 520 | _aUnderstand how to use MLOps as an engineering discipline to help with the challenges of bringing machine learning models to production quickly and consistently. This book will help companies worldwide to adopt and incorporate machine learning into their processes and products to improve their competitiveness. The book delves into this engineering discipline's aspects and components and explores best practices and case studies. Adopting MLOps requires a sound strategy, which the book's early chapters cover in detail. The book also discusses the infrastructure and best practices of Feature Engineering, Model Training, Model Serving, and Machine Learning Observability. Ray, the open source project that provides a unified framework and libraries to scale machine learning workload and the Python application, is introduced, and you will see how it fits into the MLOps technical stack. This book is intended for machine learning practitioners, such as machine learning engineers, and data scientists, who wish to help their company by adopting, building maps, and practicing MLOps. What You'll Learn Gain an understanding of the MLOps discipline Know the MLOps technical stack and its components Get familiar with the MLOps adoption strategy Understand feature engineering . | ||
| 650 | 0 |
_aMachine learning. _955059 |
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| 650 | 0 |
_aPython (Computer program language). _964608 |
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| 650 | 0 |
_aArtificial intelligence. _958441 |
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| 650 | 1 | 4 |
_aMachine Learning. _955059 |
| 650 | 2 | 4 |
_aPython. _947553 |
| 650 | 2 | 4 |
_aArtificial Intelligence. _958441 |
| 700 | 1 |
_aPumperla, Max. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _978813 |
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| 700 | 1 |
_aZhang, Zhe. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _978814 |
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| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9798868803758 |
| 776 | 0 | 8 |
_iPrinted edition: _z9798868803772 |
| 776 | 0 | 8 |
_iPrinted edition: _z9798868810862 |
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/979-8-8688-0376-5 |
| 912 | _aZDB-2-CWD | ||
| 912 | _aZDB-2-SXPC | ||
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_c60785 _d60785 |
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