InfoQ Homepage QCon ai 2019 SF Content on InfoQ
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Papers in Production Lightning Talks
Papers: Towards a Solution to the Red Wedding Problem, A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise, and A Machine Learning Approach to Databases Indexes.
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Debuggable Deep Learning
Mantas Matelis and Avesh Singh explain how they debugged DeepHeart, a DNN that detects cardiovascular disease from heart rate data.
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The Evolution of Spotify Home Architecture
Emily Samuels and Anil Muppalla discuss the evolution of Spotify's architecture that serves recommendations (playlist, albums, etc) on the Home Tab.
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Scaling Deep Learning to Petaflops and beyond!
Prabhat explores 2D and 3D convolutional architectures for solving pattern classification, regression and segmentation problems in high-energy physics, cosmology and climate science.
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Scaling Emerging AI Applications with Ray
Peter Schafhalter discusses about his work with Ray, a distributed execution framework for emerging AI applications, Tune, and Modin.
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Machine Learning Engineering - A New Yet Not So New Paradigm
Sravya Tirukkovalur discusses how ML engineering leverages skills from other engineering branches such as principles and tools, development and testing practices, and others.
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wav2letter++: Facebook's Fast Open-Source Speech Recognition System
Vitaliy Liptchinsky introduces wav2letter++, an open-source deep learning speech recognition framework, explaining its architecture and design, and comparing it to other speech recognition systems.
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Reinforcement Learning: Not Just for Robots and Games
Jibin Liu presents one of his projects at eBay where the team used RL to improve crawling of targeted web pages, starting from the basics of RL, then to why and how to use it to power web crawling.
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Petastorm: A Light-Weight Approach to Building ML Pipelines
Yevgeni Litvin describes how Petastorm facilitates tighter integration between Big Data and Deep Learning worlds, simplifies data management and data pipelines, and speeds up model experimentation.
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Applying Deep Learning to Airbnb Search
Malay Haldar discusses the work done in applying neural networks at Airbnb to improve the search beyond the results obtained with ML.
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People You May Know: Fast Recommendations over Massive Data
Sumit Rangwala and Felix GV present the evolution of PYMK’s architecture, focusing on Gaia, a real-time graph computing capability, and Venice, an online feature store with scoring capability.
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Ludwig: A Code-Free Deep Learning Toolbox
Piero Molino introduces Ludwig, a deep learning toolbox that allows to train models and to use them for prediction without the need to write code.
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