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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2304.13033 |
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| _version_ | 1866911948632752128 |
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| author | Golovin, Daniel Bartok, Gabor Chen, Eric Donahue, Emily Huang, Tzu-Kuo Kokiopoulou, Efi Qin, Ruoyan Sarda, Nikhil Sybrandt, Justin Tjeng, Vincent |
| author_facet | Golovin, Daniel Bartok, Gabor Chen, Eric Donahue, Emily Huang, Tzu-Kuo Kokiopoulou, Efi Qin, Ruoyan Sarda, Nikhil Sybrandt, Justin Tjeng, Vincent |
| contents | In many software systems, heuristics are used to make decisions - such as cache eviction, task scheduling, and information presentation - that have a significant impact on overall system behavior. While machine learning may outperform these heuristics, replacing existing heuristics in a production system safely and reliably can be prohibitively costly. We present SmartChoices, a novel approach that reduces the cost to deploy production-ready ML solutions for contextual bandits problems. SmartChoices' interface cleanly separates problem formulation from implementation details: engineers describe their use case by defining datatypes for the context, arms, and feedback that are passed to SmartChoices APIs, while SmartChoices manages encoding & logging data and training, evaluating & deploying policies. Our implementation codifies best practices, is efficient enough for use in low-level applications, and provides valuable production features off the shelf via a shared library. Overall, SmartChoices enables non-experts to rapidly deploy production-ready ML solutions by eliminating many sources of technical debt common to ML systems. Engineers have independently used SmartChoices to improve a wide range of software including caches, batch processing workloads, and UI layouts, resulting in better latency, throughput, and click-through rates. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_13033 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | SmartChoices: Augmenting Software with Learned Implementations Golovin, Daniel Bartok, Gabor Chen, Eric Donahue, Emily Huang, Tzu-Kuo Kokiopoulou, Efi Qin, Ruoyan Sarda, Nikhil Sybrandt, Justin Tjeng, Vincent Software Engineering Machine Learning In many software systems, heuristics are used to make decisions - such as cache eviction, task scheduling, and information presentation - that have a significant impact on overall system behavior. While machine learning may outperform these heuristics, replacing existing heuristics in a production system safely and reliably can be prohibitively costly. We present SmartChoices, a novel approach that reduces the cost to deploy production-ready ML solutions for contextual bandits problems. SmartChoices' interface cleanly separates problem formulation from implementation details: engineers describe their use case by defining datatypes for the context, arms, and feedback that are passed to SmartChoices APIs, while SmartChoices manages encoding & logging data and training, evaluating & deploying policies. Our implementation codifies best practices, is efficient enough for use in low-level applications, and provides valuable production features off the shelf via a shared library. Overall, SmartChoices enables non-experts to rapidly deploy production-ready ML solutions by eliminating many sources of technical debt common to ML systems. Engineers have independently used SmartChoices to improve a wide range of software including caches, batch processing workloads, and UI layouts, resulting in better latency, throughput, and click-through rates. |
| title | SmartChoices: Augmenting Software with Learned Implementations |
| topic | Software Engineering Machine Learning |
| url | https://arxiv.org/abs/2304.13033 |