InfoQ Homepage QCon ai 2018 Content on InfoQ
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Liquidity Modeling in Real Estate Using Survival Analysis
Xinlu Huang and David Lundgren discuss hazard and survival modeling, metrics, and data censoring, describing how Opendoor uses these models to estimate holding times for homes and mitigate risk.
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Data Pipelines for Real-Time Fraud Prevention at Scale
Mikhail Kourjanski discusses the architecture of PayPal’s data service which combines a Big Data approach with providing data in real time for decision making in fraud detection.
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pDB: Scalable Prediction Infrastructure with Precision and Provenance
Balaji Rengarajan describes the platform built on the Celect’s pDB framework, providing multiple use cases such as online personalization, document classification, and geospatial anomaly detection.
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Self-Racing Using Deep Neural Networks: Lap 2
Jendrik Joerdening and Anthony Navarro discuss how a team of Udacity students used neural networks to teach a car to drive by itself around a track in two days.
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The Black Swan of Perfectly Interpretable Models
Mayukh Bhaowal, Leah McGuire discuss how Salesforce Einstein made ML more transparent and less of a black box, and how they managed to drive wider adoption of ML.
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Counting is Hard: Probabilistic Algorithms for View Counting at Reddit
Krishnan Chandra explains the challenges of building a view counting system at scale, and how Reddit used probabilistic counting algorithms to make scaling easier.
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Developing Data and ML Pipelines at Stitch Fix
Jeff Magnusson discusses thoughts and guidelines on how Stitch Fix develops, schedules, and maintains their data and ML pipelines.
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Counterfactual Evaluation of Machine Learning Models
Michael Manapat discusses how Stripe evaluates and trains their machine learning models to fight fraud.
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Machine Learning Pipeline for Real-Time Forecasting @Uber Marketplace
Chong Sun and Danny Yuan discuss how Uber is using ML to improve their forecasting models, the architecture of their ML platform, and lessons learned running it in production.
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