InfoQ Homepage Programming Content on InfoQ
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Our Concurrent Past; Our Distributed Future
Joe Duffy talks about the concurrency's explosion onto the mainstream over the past 15 years and attempts to predict what lies ahead for distributed programming, from now til 15 years into the future.
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Deep Learning at Scale
Scott Le Grand describes his work at NVidia, Amazon and Teza, including the DSSTNE distributed deep learning framework.
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Reactive & Asynchronous - Adventures with APIs in Financial Trading
Michael Barker discusses several low-latency APIs used for financial trading, what makes them fast and how they compare to HTTP/REST/JSON/XML APIs.
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Using NLP, Machine Learning & Deep Learning Algorithms to Extract Meaning from Text
David Talby walks through building a natural language annotations pipeline with domain-specific annotators, and using deep learning to automatically expand and update taxonomies.
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Engineering You
Lynn Langit and Martin Thompson explore the individual practices and techniques that can help bring out the engineer in us.
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The Move to AI: from HFT to Laplace Demon
Eric Horesnyi and Albert Bifet discuss how hedge funds have moved beyond High Frequency Trading using AI and real-time data processing.
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Building Data Pipelines in Python
Marco Bonzanini discusses the process of building data pipelines and all the steps necessary to prepare data, focusing on data plumbing and going from prototype to production.
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Policing the Stock Market with Machine Learning
Cliff Click talks about SCORE, a solution for doing Trade Surveillance using H2O, Machine Learning, and a whole lot of domain expertise and data munging.
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API Design Aesthetics
Col Perks looks at API design as a style, considering the qualities that might make an API beautiful and providing real world examples.
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Freeing the Whale: How to Fail at Scale
Oliver Gould discusses Finagle, a library providing a uniform model for handling failure at the communications layer, enabling Twitter to fail, safely and often.
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Data Cleansing and Understanding Best Practices
Casey Stella talks about discovering missing values, values with skewed distributions and likely errors within data, as well as a novel approach to finding data interconnectedness.
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From Data Science to Production–Deploy, Scale, Enjoy
Sergii Khomenko introduces best practices in development, covers production deployments to the AWS stack, and using the serverless architecture for data applications.
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