InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Beyond Chatbots: The Future of AI and Business
Will Murphy explores chatbots, the use of AI and what’s in store for businesses using them in the future.
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Data Preparation for Data Science: A Field Guide
Casey Stella presents a utility written with Apache Spark to automate data preparation, discovering missing values, values with skewed distributions and discovering likely errors within data.
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AI from an Investment Perspective
The panelists discuss AI from an investment perspective, the challenges, the risks, trends, the role of Deep Learning, successful AI use cases, and more.
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Machine Learning at Scale
Aditya Kalro discusses using large-scale data for Machine Learning (ML) research and some of the tools Facebook uses to manage the entire process of training, testing, and deploying ML models.
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Straggler Free Data Processing in Cloud Dataflow
Eugene Kirpichov describes the theory and practice behind Cloud Dataflow's approach to straggler elimination, and the associated non-obvious challenges, benefits, and implications of the technique.
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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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AI in Medicine
Anthony Chang presents the current status of AI in medicine and the foreseeable future in front of it.
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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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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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Big Data Infrastructure @ LinkedIn
Shirshanka Das describes LinkedIn’s Big Data Infrastructure and its evolution through the years, including details on the motivation and architecture of Gobblin, Pinot and WhereHows.
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