InfoQ Homepage QCon ai 2018 Content on InfoQ
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R for AI developers
David Smith makes the case for R as a platform for developing models for intelligent applications, offering a few examples with details in the accompanying interactive code lab.
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Google Dataflow Codelab
Martin Gorner introduces Google Dataflow Codelab, and demos the tools and techniques they are using for data science.
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Inside a Self-driving Uber
Matt Ranney discusses the software components that come together to make a self-driving Uber drive itself, and how they test new software before it is deployed to the fleet.
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Streaming SQL to Unify Batch & Stream Processing w/ Apache Flink @Uber
Shuyi Chen and Fabian Hueske explore SQL’s role in the world of streaming data and its implementation in Apache Flink and covering streaming semantics, event time, and incremental results.
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Simplifying ML Workflows with Apache Beam
Tyler Akidau discusses how Apache Beam is simplifying pre- and post-processing for ML pipelines.
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Gimel: PayPal’s Analytics Data Platform
Deepak Chandramouli introduces and demos Gimel, a unified analytics data platform which provides access to any storage through a single unified data API and SQL.
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Understanding Software System Behavior with ML and Time Series Data
David Andrzejewski discusses how time series datasets can be combined with ML techniques in order to aid in the understanding of system behaviors in order to improve performance and uptime.
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Analyzing & Preventing Unconscious Bias in Machine Learning
Rachel Thomas keynotes on three case studies, attempting to diagnose bias, identify some sources, and discusses what it takes to avoid it.
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Understanding ML/DL Models using Interactive Visualization Techniques
Chakri Cherukuri discusses how to use visualization techniques to better understand machine learning and deep learning models.
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Interpretable Machine Learning Products
Mike Lee Williams discusses how interpretability can make deep neural networks models easier to understand, and describes LIME, an OS tool that can be used to explore what ML classifiers are doing.
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End-to-End ML without a Data Scientist
Holden Karau discusses how to train models, and how to serve them, including basic validation techniques, A/B tests, and the importance of keeping models up-to-date.
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Deep Learning for Science
Prabhat discusses machine learning's impact on climatology, astronomy, cosmology, neuroscience, genomics, and high-energy physics, and the future of AI in powering scientific discoveries.
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