InfoQ Homepage Machine Learning Content on InfoQ
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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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Understanding Deep Learning
Jessica Yung talks about the foundational concepts about neural networks and highlights key things to pay attention to: learning rates, how to initialize a network, and more.
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Intuition & Use-Cases of Embeddings in NLP & beyond
Jay Alammar talks about the concept of word embeddings, how they're created, and looks at examples of how these concepts can be carried over to solve problems.
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How to Prevent Catastrophic Failure in Production ML Systems
Martin Goodson describes the unpredictable nature of artificial intelligence systems and how mastering a handful of engineering principles can mitigate the risk of failure.
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Productionizing H2O Models with Apache Spark
Jakub Hava demonstrates the creation of pipelines integrating H2O machine learning models and their deployments using Scala or Python.
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