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For the final Database Seminar of this quarter on Friday (Dec 4) at 3pm PT, we have another exciting external speaker, Alex Ratner. He is going to tell us about programmatic approaches to label data for modern ML/AI applications that reduce the burden of manual hand labeling.
Please find the talk details and Zoom details below.
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Programmatically Building & Managing Training Data with Snorkel
One of the key bottlenecks in building machine learning systems is creating and managing the massive training datasets that today’s models require. In this talk, I will describe our work on Snorkel (snorkel.org), an open-source framework for building and managing training datasets, and describe three key operators for letting users build and manipulate training datasets: labeling functions, for labeling unlabeled data; transformation functions, for expressing data augmentation strategies; and slicing functions, for partitioning and structuring training datasets. These operators allow domain expert users to specify machine learning (ML) models entirely via noisy operators over training data, expressed as simple Python functions—or even via higher level NL or point-and-click interfaces—leading to applications that can be built in hours or days, rather than months or years, and that can be iteratively developed, modified, versioned, and audited. I will describe recent work on modeling the noise and imprecision inherent in these operators, and using these approaches to train ML models that solve real-world problems, including recent state-of-the-art results on benchmark tasks and real-world industry, government, and medical deployments.
Alex Ratner is the co-founder and CEO of Snorkel AI, Inc., which supports the open source Snorkel library and develops Snorkel Flow, an end-to-end system for building machine learning applications, and an Assistant Professor of Computer Science at the University of Washington. Prior to Snorkel AI and UW, he completed his PhD in CS advised by Christopher Ré at Stanford, where his research focused on applying data management and statistical learning techniques to emerging machine learning workflows, such as creating and managing training data, and applying this to real-world problems in medicine, knowledge base construction, and more.
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