InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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The Fast Track to AI with JavaScript and Serverless
Peter Elger explores how to get started building AI enabled platforms and services using full stack JavaScript and Serverless technologies.
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Is Machine Learning the Right Tool?
Brian Korzynski discusses when and where using machine learning will fit within projects.
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Visual Intro to Machine Learning and Deep Learning
Jay Alammar offers a mental map of Machine Learning prediction models and how to apply them to real-world problems with many examples from existing businesses and products.
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From Mainframe to Microservices with Pivotal Platform and Kafka: Bridging the Data Divide
Dmitry Milman and Ankur Kaneria showcase how Pivotal and Apache Kafka are leveraged within Express Scripts’ transformation from mainframe to a microservices-based ecosystem, ensuring data integrity.
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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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