InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Taming Large State: Lessons from Building Stream Processing
Sonali Sharma and Shriya Arora describe how Netflix solved a complex join of two high-volume event streams using Flink.
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Deep Learning at Scale: Distributed Training and Hyperparameter Search for Image Recognition Problems
Michael Shtelma discusses methods and libraries for training models on a dataset that does not fit into memory or maybe even on the disk using multiple GPUs or even nodes.
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Building a Data Exchange with Spring Cloud Data Flow
Channing Jackson presents a case study in the distillation of the finite patterns on each side of the data exchange and a discussion of the patterns used.
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Machine Learning through Streaming at Lyft
Sherin Thomas talks about the challenges of building and scaling a fully managed, self-service platform for stream processing using Flink, best practices, and common pitfalls.
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Probabilistic Programming for Software Engineers
Michael Tingley provides a preview of how Facebook is advancing probabilistic programming, as well as some of the big problems they used it to solve.
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Anti-Entropy Using CRDTs on HA Datastores @Netflix
Sailesh Mukil briefly introduces Dynomite, offers a deep dive on how anti-entropy is implemented and talks about the underlying principles of CRDTs that make this possible.
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The Joy of Designing Deep Neural Networks
Bradley Arsenault shares the joy he felt the first time he designed a deep neural network, and how simple intuitions on neural networks have led to greater designs and accuracy.
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From Batch to Streaming to Both
Herman Schaaf talks about Skyscanner’s journey to implement their data platform to stream and store millions of events per second.
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Kafka: A Modern Distributed System
Tim Berglund covers Kafka's distributed system fundamentals: the role of the Controller, the mechanics of leader election, and the role of Zookeeper today and in the future.
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We Also Can Do It! Machine Learning in Javascript!
Eliran Eliassy shows how to create a prediction model with a web application using TensorFlow.js and other deep learning tools that can run in the browser.
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You Can AI Like an Expert
Jon McLoone shows that symbolic representation also helps in automating the transition from research experiments to the production deployment of AI services.
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Machine Learning on Mobile and Edge Devices with TensorFlow Lite
Daniel Situnayake talks about how developers can use TensorFlow Lite to build machine learning applications that run entirely on-device.
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