Designing machine learning systems: an iterative process for production-ready applications
Many tutorials show you how to develop ML systems from ideation to deployed models. But with constant changes in tooling, those systems can quickly become outdated. Without an intentional design to hold the components together, these systems will become a technical liability, prone to errors and be...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo
O'Reilly
May 2022
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| Ausgabe: | First edition |
| Schlagworte: | |
| Online-Zugang: | DE-188 DE-523 DE-2070s DE-355 |
| Zusammenfassung: | Many tutorials show you how to develop ML systems from ideation to deployed models. But with constant changes in tooling, those systems can quickly become outdated. Without an intentional design to hold the components together, these systems will become a technical liability, prone to errors and be quick to fall apart. In this book, Chip Huyen provides a framework for designing real-world ML systems that are quick to deploy, reliable, scalable, and iterative. These systems have the capacity to learn from new data, improve on past mistakes, and adapt to changing requirements and environments. Youâ??ll learn everything from project scoping, data management, model development, deployment, and infrastructure to team structure and business analysis. Learn the challenges and requirements of an ML system in production Build training data with different sampling and labeling methods Leverage best techniques to engineer features for your ML models to avoid data leakage Select, develop, debug, and evaluate ML models that are best suit for your tasks Deploy different types of ML systems for different hardware Explore major infrastructural choices and hardware designs Understand the human side of ML, including integrating ML into business, user experience, and team structure... |
| Beschreibung: | 1 Online-Ressource (xvi, 367 Seiten) Illustrationen, Diagramme |
| ISBN: | 9781098107932 |
Internformat
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Datensatz im Suchindex
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| author | Huyen, Chip |
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| id | DE-604.BV048277459 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T20:00:31Z |
| indexdate | 2024-09-20T04:22:21Z |
| institution | BVB |
| isbn | 9781098107932 |
| language | English |
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| physical | 1 Online-Ressource (xvi, 367 Seiten) Illustrationen, Diagramme |
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| spelling | Huyen, Chip Verfasser (DE-588)1261904311 aut Designing machine learning systems an iterative process for production-ready applications Chip Huyen First edition Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo O'Reilly May 2022 1 Online-Ressource (xvi, 367 Seiten) Illustrationen, Diagramme txt rdacontent c rdamedia cr rdacarrier Many tutorials show you how to develop ML systems from ideation to deployed models. But with constant changes in tooling, those systems can quickly become outdated. Without an intentional design to hold the components together, these systems will become a technical liability, prone to errors and be quick to fall apart. In this book, Chip Huyen provides a framework for designing real-world ML systems that are quick to deploy, reliable, scalable, and iterative. These systems have the capacity to learn from new data, improve on past mistakes, and adapt to changing requirements and environments. Youâ??ll learn everything from project scoping, data management, model development, deployment, and infrastructure to team structure and business analysis. Learn the challenges and requirements of an ML system in production Build training data with different sampling and labeling methods Leverage best techniques to engineer features for your ML models to avoid data leakage Select, develop, debug, and evaluate ML models that are best suit for your tasks Deploy different types of ML systems for different hardware Explore major infrastructural choices and hardware designs Understand the human side of ML, including integrating ML into business, user experience, and team structure... Systementwurf (DE-588)4261480-6 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Systementwurf (DE-588)4261480-6 s DE-604 Erscheint auch als Druck-Ausgabe 978-1-098-10796-3 (DE-604)BV048219598 |
| spellingShingle | Huyen, Chip Designing machine learning systems an iterative process for production-ready applications Systementwurf (DE-588)4261480-6 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4261480-6 (DE-588)4193754-5 |
| title | Designing machine learning systems an iterative process for production-ready applications |
| title_auth | Designing machine learning systems an iterative process for production-ready applications |
| title_exact_search | Designing machine learning systems an iterative process for production-ready applications |
| title_exact_search_txtP | Designing machine learning systems an iterative process for production-ready applications |
| title_full | Designing machine learning systems an iterative process for production-ready applications Chip Huyen |
| title_fullStr | Designing machine learning systems an iterative process for production-ready applications Chip Huyen |
| title_full_unstemmed | Designing machine learning systems an iterative process for production-ready applications Chip Huyen |
| title_short | Designing machine learning systems |
| title_sort | designing machine learning systems an iterative process for production ready applications |
| title_sub | an iterative process for production-ready applications |
| topic | Systementwurf (DE-588)4261480-6 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Systementwurf Maschinelles Lernen |
| work_keys_str_mv | AT huyenchip designingmachinelearningsystemsaniterativeprocessforproductionreadyapplications |