InfoQ Homepage QCon Software Development Conference Content on InfoQ
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Effective Data Pipelines: Data Mngmt from Chaos
Katharine Jarmul discusses implementation decisions for those looking for a practical recommendation on the "what" and "how" of data automation workflows.
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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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Enabling Awesome Engineering Teams
Alexandre Freire discusses what his team has learned through experiments, from the very fundamentals of what a team needs to be successful to Modern Agile engineering techniques.
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Stream Processing & Analytics with Flink @Uber
Danny Yuan discusses how Uber builds its next generation of stream processing system to support real-time analytics as well as complex event processing.
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Demistifying DynamoDB Streams
Akshat Vig and Khawaja Shams discuss DynamoDB Streams and what it takes to build an ordered, highly available, durable, performant, and scalable replicated log stream.
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How to Design and Develop in an Inclusive Way
Molly Watt and Chris Bush discuss designing for people with specific visual, auditory, cognitive and mobility needs, accessibility features and challenges for certain users engaging digital services.
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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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Building a Data Science Capability from Scratch
Victor Hu covers the challenges, both technical and cultural, of building a data science team and capability in a large, global company.
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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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Predictability in ML Applications
Claudia Perlich presents scenarios in which the combination of different and highly informative features can have significantly negative overall impact on the usefulness of predictive modeling.
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