InfoQ Homepage Machine Learning Content on InfoQ
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Bringing Machine Learning to Every Corner of Your Business
Danny Lange presents Uber’s Machine Learning service that can perform functions such as ETA, fraud detection, churn prediction, forecasting demand, and much more.
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Achieving Mega-Scale Business Intelligence through Speed of Thought Analytics on Hadoop
Ian Fyfe discusses the different options for implementing speed-of-thought business analytics and machine learning tools directly on top of Hadoop.
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Developing a Machine Learning Based Predictive Analytics Engine for Big Data Analytics
Ali Jalali presents how to develop a machine learning predictive analytics engine for big data analytics.
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Building an AI in the Cloud
Simon Chan shares the on-going challenges, the design dilemma and the steps to be taken when building customized large-scale predictive ML applications on a ML SaaS platform.
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Machine Learning Fast and Slow
Suman Deb Roy talks about some of Betaworks’ internal data tools and platform, product-specific solutions and best practices they learned when machine learning has to drive the startup road.
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Vowpal Wabbit, A Machine Learning System
John Langford discusses how to use Vowpal Wabbit in and as a machine learning system including architecture, unique capabilities, and applications, applied to personalized news recommendation.
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Online Data Mining and Machine Learning
Edo Liberty presents some basic concepts and an introduction to the subfields of machine learning and data mining.
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Hunting Criminals with Hybrid Analytics
David Talby demos using Python libraries to build a ML model for fraud detection, scaling it up to billions of events using Spark, and what it took to make the system perform and ready for production.
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How Comcast Uses Data Science and ML to Improve the Customer Experience
Jan Neumann presents how Comcast uses machine learning and big data processing to facilitate search for users, for capacity planning, and predictive caching.
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Startup ML: Bootstrapping a Fraud Detection System
Michael Manapat talks about how to choose, train, and evaluate models, how to bridge the gap between training and production systems, and avoiding pitfalls.
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Artificial Intelligence that Plays Atari Video Games: How Did Deep Mind Do It?
Kristjan Korjus discusses deep learning, reinforcement learning and their combination called deep Q-Network.
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Insights from History of Rock Music via Machine Learning
Ali Kheyrollahi uses clustering and network analysis algorithms to analyze the publicly available Wiki data on rock music to find mathematical relationship between artists, trends and subgenres.
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