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Building, monitoring, and improving machine learning systems is no walk in the park, no matter the circumstances. Data scientists and engineers have to monitor fine-grained quality and diagnose errors in sophisticated apps, not to mention field contradictory or incomplete corpora. To ease the development burden somewhat, Apple developed Overton, a framework intended to automate AI system lifecycles by providing a set of novel high-level abstractions. Given the query “How tall is the president of the United States,” for example, Overton generates a model capable of supplying an answer. (It only supports text processing currently, but Apple is prototyping image, video, and multimodal apps.)

Apple researchers say that Overton has been used in production to support “multiple applications” in both near-real-time and back-of-house processing, and in that time, Overton-based apps have answered “billions” of queries in multiple languages and processed “trillions” of records. “[The] vision is to shift developers to … higher-level tasks instead of lower-level machine learning tasks. [E]ngineers can build deep-learning-based applications without writing any code,” wrote the coauthors of a research paper describing Overton. “Overton [can] automate many of the traditional modeling choices, including deep learning architecture … and [it allows engineer] … to build, maintain, and monitor their application by manipulating data files.”

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