InfoQ Homepage Modeling Content on InfoQ
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Simulating Agile Strategies
Adam Timlett introduces the Lazy Stopping Model, simulating different strategies for software development or capital projects, explaining how it works, the ideas behind it and what it can be used for.
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Metrics-Driven Machine Learning Development at Salesforce Einstein
Eric Wayman discusses how Salesforce tracks data and modeling metrics in the pipeline to identify data and modeling issues and to raise alerts for issues affecting models running in production.
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End-to-End ML without a Data Scientist
Holden Karau discusses how to train models, and how to serve them, including basic validation techniques, A/B tests, and the importance of keeping models up-to-date.
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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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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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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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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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When Models Go Rogue: Hard Earned Lessons on Using Machine Learning in Production
David Talby summarizes best practices & lessons learned in ML, based on nearly a decade of experience building & operating ML systems at Fortune 500 companies across several industries.
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Causal Inference in Data Science
Amit Sharma discusses the value of counterfactual reasoning and causal inference, demonstrating that relying on predictive modeling based on correlations can be counterproductive.
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Predictability in ML Applications
Claudia Perlich presents scenarios in which the combination of different and highly informative features can have significantly negative overall impact on the usefulness of predictive modeling.
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Framing Our Potential for Failure
Michelle Brush discusses modeling complex systems and architectural changes that could introduce new modes of failure, using examples from embedded systems to large stream processing pipelines.
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