InfoQ Homepage Machine Learning Content on InfoQ
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Fairness, Transparency, and Privacy in AI @LinkedIn
Krishnaram Kenthapadi focuses on the application of privacy-preserving data mining and fairness-aware ML techniques in practice, by presenting case studies spanning different LinkedIn applications.
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Michelangelo - Machine Learning @Uber
Jeremy Hermann talks about Michelangelo - the Machine Learning Platform that powers most of the machine learning solutions at Uber.
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Creating Robust Interpretable NLP Systems with Attention
Alexander Wolf introduces Attention, an interpretable type of neural network layer that is loosely based on attention in human, explaining why and how it has been utilized to revolutionize NLP.
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Monitoring AI with AI
Iskandar Sitdikov discusses a solution, tooling and architecture that allows an ML engineer to be involved in delivery phase and take ownership over deployment and monitoring of ML pipelines.
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Migrating ML from Research to Production
Conrado Silva Miranda shares his experience leveraging research to production settings, presenting the major issues faced by developers and how to establish stable production for research.
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Building a Voice Assistant for Enterprise
Manju Vijayakumar talks about Einstein Assistant - an AI Voice assistant for enterprises that enables users to "Talk to Salesforce".
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Reasoning about Uncertainty at Scale
Max Livingston presents a case study of using Bayesian modelling and inference to directly model behavior of aircraft arrivals and departures, focusing on the uncertainty in those predictions.
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Machines Can Learn - a Practical Take on Machine Intelligence Using Spring Cloud Data Flow and TensorFlow
Christian Tzolov showcases how building a complex use-case, such as real-time image recognition or object detection, can be simplified with the help of the Spring Ecosystem and TensorFlow.
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Zero to Production in Five Months @ ThirdLove
Megan Cartwright discusses how ThirdLove built their first machine learning recommendation algorithm that predicts bra size and style.
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Designing Automated Pipelines for Unseen Custom Data
Kevin Moore discusses some challenges in designing automated machine learning pipelines that can deal with custom user data that it has never seen before, as well as some of Salesforce’s solutions.
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Machine Learning Interpretability in the GDPR Era
Gregory Antell explores the definition of interpretability in ML, the trade-offs with complexity and performance, and surveys the major methods used to interpret and explain ML models in the GDPR era
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Nearline Recommendations for Active Communities @LinkedIn
Hema Raghavan focusses on technologies they have built to power LinkedIn’s “People You May Know” product and describes their nearline platform for notification recommendation.
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