InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Improving Developer Productivity with Visual Studio Intellisense
Allison Buchholtz-Au and Shengyu Fu discuss how PM, engineering, and data science came together to build Visual Studio IntelliCode, which delivers context-aware code completion suggestions.
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On a Deep Journey towards Five Nines
Aashish Sheshadri discusses how PayPal applies Seq2Seq networks to forecasting CPU and memory metrics at scale.
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Document Digitization: Rethinking OCR with Machine Learning
Nischal Harohalli Padmanabha outlines the problems faced building DL networks for document process at omni:us, limitations, the evolution of team structures, engineering practices, and other topics.
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Comparing Machine Learning Strategies Using Scikit-Learn and TensorFlow
Oliver Zeigermann looks at different ML strategies -KNN, Decision Trees, Support Vector Machines, and Neural Networks- and visualizes how they make predictions by plotting their decision boundaries.
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Code Your Way out of a Paper Bag
Frances Buontempo discusses how to program your way out of the paper bag using machine learning techniques.
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How to Create a Data Science Product from Scratch?
Dmytro Bilash discusses the top five biggest challenges in creating a data science product, compares a product for one client and a scalable one for the whole market, and how to be successful.
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From Robot Simulation to the Real World
Louise Poubel overviews Gazebo's architecture with examples of projects using Gazebo, describing how to bridge virtual robots to their physical counterparts.
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The Road to Artificial Intelligence: An Ethical Minefield
Lloyd Danzig offers a look into the complex ethical issues faced by today's top engineers and poses open-ended questions for the consideration of attendees.
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Panel: Predictive Architectures in Practice
The panelists discuss the unique challenges of building and running data architectures for predictions, recommendations and machine learning.
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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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