InfoQ Homepage QCon ai 2019 SF Content on InfoQ
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Deep Learning on Microcontrollers
Pete Warden discusses why Deep Learning is a great fit for tiny, cheap devices, what can be built with it, and how to get started.
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Practical NLP for the Real World
Emmanuel Ameisen discusses examples of how to build practical applications using NLP, diving into data visualization and labelling, as well as model validation.
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Federated Learning: Rewards & Challenges of Distributed Private ML
Eric Tramel discusses the basic concepts underlying the federated ML approach, the advantages it brings, as well as the challenges associated with constructing federated solutions.
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Michelangelo Palette: A Feature Engineering Platform at Uber
Amit Nene and Eric Chen discuss the infrastructure built by Uber for Michelangelo ML Platform that enables a general approach to Feature Engineering across diverse data systems.
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Instrumentation, Observability & Monitoring of Machine Learning Models
Josh Wills discusses the monitoring and visibility needs of machine learning models in order to bridge gaps between ML practitioners and DevOps.
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Privacy: The Last Stand for Fair Algorithms
Katharine Jarmul discusses research related to fair-and-private ML algorithms and privacy-preserving models, showing that caring about privacy can help ensure a better model overall and support ethics
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The Future of Transportation
Anita Sengupta discusses the future of transportation with an eye towards how machine learning and AI will help shape the future.
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Massive Scale Anomaly Detection Framework
Guy Gerson introduces an anomaly detection framework PayPal uses, focusing on flexibility to support different types of statistical and ML models, and inspired by scikit-learn and Spark MLlib.
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Modern NLP for Pre-Modern Practitioners
Joel Grus discusses the latest in NLP research breakthrough, and how to incorporate NLP concepts and models into a project.
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