InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Systems That Learn
Stephen Buckley discusses the Systems That Learn initiative which aims to create systems that learn by combining expertise in Systems and Machine Learning.
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Homoiconicity: It Is What It Is
Stuart Sierra demonstrates the power that comes from having the same data representation at all layers: programming language, specification, database, inter-process communication, and user interface.
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Best Trade-off Point Algorithm for Efficient Resource Provisioning in Hadoop
Peter Nghiem presents the Best Trade-off Point method and algorithm with mathematical formulas for obtaining the exact optimal number of task resources for any workload running on Hadoop.
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Primer on Neural Networks
Chase Aucoin introduces neural networks with examples and simple breakdowns about the math involved in a way accessible to a large audience.
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Online Learning & Custom Decision Services
Markus Cozowicz and John Langford talk about the new system they have created which automates exploit-explore strategies, data gathering, and learning to create useable online interactive learning.
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Improving Both the UX and DX with White Box AI
Cloderic Mars discusses using white box AI to improve user and developer experience.
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Machine Learning in Academia and Industry
Deborah Hanus discusses some of the challenges that can arise when working with data.
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Automating Inventory at Stitch Fix
Sally Langford talks about the use of ML within StitchFix’s inventory forecasting system, the architecture they have developed in-house and their use of Bayesian methods.
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Solving Payment Fraud and User Security with ML
Soups Ranjan talks about Coinbase’s risk program that relies on machine learning (supervised and unsupervised), rules-based systems as well as highly-skilled human fraud fighters.
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Causal Modeling Using Software Called TETRAD V
Suchitra Abel introduces TETRAD and some of its components used for causal modeling to find out the proper causes and effects of an event.
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Evaluating Machine Learning Models: A Case Study
Nelson Ray talks about on how to estimate the business impact of launching various machine learning models, in particular, those Opendoor uses for modeling the liquidity of houses.
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Deep Learning @Google Scale: Smart Reply in Inbox
Anjuli Kannan describes the algorithmic, scaling, deployment considerations involved in a an application of cutting-edge deep learning in a user-facing product: the Smart Reply feature of Google Inbox
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