InfoQ Homepage Machine Learning Content on InfoQ
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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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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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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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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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