Although machine learning (ML) and deep learning (DL) provide new possibilities into optimizing system design and performance, taking advantage of this paradigm shift requires more than implementing existing ML\/DL algorithms. This paper reports our years of experience in designing and operating several production learning-augmented systems at Microsoft. AutoSys is a framework that unifies the development process, and it addresses common design considerations including ad-hoc and nondeterministic jobs, learning-induced system failures, and programming extensibility. Furthermore, this paper demonstrates the benefits of adopting AutoSys with measurements from one production system, Web Search. Finally, we share long-term lessons stemmed from unforeseen implications that have surfaced over the years of operating learning-augmented systems.<\/p>\n","protected":false},"excerpt":{"rendered":"
Although machine learning (ML) and deep learning (DL) provide new possibilities into optimizing system design and performance, taking advantage of this paradigm shift requires more than implementing existing ML\/DL algorithms. This paper reports our years of experience in designing and operating several production learning-augmented systems at Microsoft. AutoSys is a framework that unifies the development 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