InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Everything You Wanted to Know about Apache Kafka But Were Too Afraid to Ask!
Ricardo Ferreira explains what a streaming platform such as Apache Kafka is and some of the use cases and design patterns around its use.
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Accuracy as a Failure
V. Warmerdam talks about cautionary tales of mistakes that might happen when we let data scientists on a goose chase for accuracy. Highly accurate models are more damaging than the inaccurate ones
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High-Performance Data Processing with Spring Cloud Data Flow and Geode
Cahlen Humphreys and Tiffany Chang discuss why Enfuse.io chose Apache Geode and Pivotal Cloud Cache for their data processing needs.
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Predicting Cryptocurrency Exchange Rates with Stream Processing, Social Data and Online Learning
Tim Frey discusses how iunera used social data from Twitter in machine learning to predict crypto currency exchange rates.
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Databases and Stream Processing: a Future of Consolidation
Ben Stopford digs into why both stream processors and databases are necessary from a technical standpoint but also by exploring industry trends that make consolidation in the future far more likely.
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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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