InfoQ Homepage QCon London 2017 Content on InfoQ
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Extreme Programming Meets Real-time Data
Tom Johnson and Gel Goldsby talk about scaling problems they encountered at Unruly, and where extreme programming values led them.
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Products and Prototypes with Keras
Micha Gorelick shows how to build a working product with Keras, a high-level deep learning framework, discussing design decisions, and demonstrating how to train and deploy a model.
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Causal Consistency for Large Neo4j Clusters
Jim Webber explores the new Causal clustering architecture for Neo4j, how it allows users to read writes straightforwardly, explaining why this is difficult to achieve in distributed systems.
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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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Building Robust Machine Learning Systems
Stephen Whitworth talks about his experience at Ravelin, and provides useful practices and tips to help ensure our machine learning systems are robust, well audited, avoid embarrassing predictions.
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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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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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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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