InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Automating Inventory at Stitch Fix
Sally Langford talks about the use of ML within StitchFix’s inventory forecasting system, the architecture they have developed in-house and their use of Bayesian methods.
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Solving Payment Fraud and User Security with ML
Soups Ranjan talks about Coinbase’s risk program that relies on machine learning (supervised and unsupervised), rules-based systems as well as highly-skilled human fraud fighters.
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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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Deep Learning @Google Scale: Smart Reply in Inbox
Anjuli Kannan describes the algorithmic, scaling, deployment considerations involved in a an application of cutting-edge deep learning in a user-facing product: the Smart Reply feature of Google Inbox
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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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