InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Semi-Supervised Deep Learning on Large Scale Climate Models
Prabhat presents NERSc’s results in applying Deep Learning for supervised and semi-supervised learning of extreme weather patterns, scaling Deep Learning to 9000 KNL nodes on a supercomputer.
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Do We Need Another Key-Value Store?
Hendrik Muhs introduces Keyvi, a key-value store based on 'finite state', describing the concepts, explaining what makes it different and where it is useful.
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Enabling High Performance Real-time Analytics for IoT Environments
Mahish Singh discusses how to use methodologies during design, development, deployment and operation for delivery of analytics platforms which offer real-time SLAs.
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Architecture & Algorithms Powering Search @ZocDoc
Brian D'Alessandro and Pedro Rubio talk about the patient friendly search system they have built at Zocdoc using various products from the AWS stack and custom Machine Learning pipelines.
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Precision Measurements in eCommerce
Jennifer Prendki showcases how precision measurements will allow companies like Walmart to deliver a more personalized experience in eCommerce through the combination of Big Data and hard science.
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Solving Business Problems with Data Science
The panelists discuss how companies can use data science to solve various business problems.
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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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Orchestrating Chaos: Applying Database Research in the Wild
Peter Alvaro describes LDFI’s (Lineage-driven Fault Injection) theoretical roots in database research, presenting early results from the field and opportunities for near and long-term future research.
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Scaling with Apache Spark
Holden Karau looks at Apache Spark from a performance/scaling point of view and what’s needed to handle large datasets.
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Large Scale Machine Learning for Payment Fraud Prevention
Venkatesh Ramanathan presents how advanced machine learning algorithms such as Deep Learning and Gradient Boosting are applied at PayPal for fraud prevention.
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AI as a Service at Scale: Retail Case Study
Eldar Sadikov discusses emerging applications of AI in retail, illustrating how Jetlore's machine learning rank technology is currently utilized to power millions of consumer experiences every day.
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