InfoQ Homepage Machine Learning Content on InfoQ
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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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TensorFlow: Pushing the ML Boundaries
Magnus Hyttsten talks about how Google uses Machine Learning to address problems that were not solvable a year ago, looking at models and how they can be built.
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AI & Security: Lessons and Challenges
Dawn Song presents results in the area of secure deep learning and how DL systems could be fooled and what can be done, how AI and DL can enable better security, and how security can enable better AI.
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Real-Time Decisions Using ML on the Google Cloud Platform
Przemyslaw Pastuszka and Carlos Garcia present how Big Data is handled in Google Cloud Platform to build an end-to-end machine learning pipeline.
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Artificial Intelligence and Machine Learning for the SWE
Rob Harrop describes both his own journey from traditional Software Engineer to AI/ML Engineer, and his experience building a development team with ML at the heart.
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Pivotal Cloud Foundry, Google Machine Learning, and Spring
Brian Gregory, Brian Jimerson introduce the GCP Service Broker on Pivotal Cloud Foundry and the Google Cloud Machine Learning APIs demonstrating a Spring application using the Machine Learning APIs.
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