InfoQ Homepage Presentations
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Refactoring Elixir - Lessons Learned from a Year on Exercism.Io
Devon Estes discusses some common, but less than optimal, solutions to some of the problems on exercism.io followed by refactoring, showing the performance improvements and tradeoffs made.
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Elixir and Money
Tomasz Kowal discusses using Elixir for a financial application, handling rounding errors, designing APIs that gracefully handle network and hardware failures, and crashing the app during design.
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Real-World Virtual Reality
Alex Kesling explores Google Expeditions as a case study in building meaningful Virtual Reality applications.
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Modern Distributed Optimization
Matt Adereth talks about the Black-box optimization techniques, what’s actually going on inside of these black-boxes and discusses an idea of how they can be used to solve problems today.
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What Came First: The Ordering of Events in Systems
Kavya Joshi explores the beautifully simple happens-before principle and delves into how happens-before is tracked in a distributed database like Riak.
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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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API Design Lessons Learned: Enterprise to Startup
Mohamed El-Geish explores lessons learned at big companies like Microsoft and LinkedIn, and adapts the insights drawn from them to fit a fast-growing startup.
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