InfoQ Homepage Machine Learning Content on InfoQ
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Designing Better ML Systems: Learnings from Netflix
Savin Goyal shares lessons learned by Netflix building their ML infrastructure, and some of the tradeoffs to consider when designing or buying a machine learning system.
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BERT for Sentiment Analysis on Sustainability Reporting
Susanne Groothuis discusses how KPMG created a custom sentiment analysis model capable of detecting subtleties, and provides them with a metric indicating the balance of a report.
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Applying Machine Learning to Financial Payments
Tamsin Crossland discusses how ML can be applied to Payments to respond rapidly to known and emerging patterns of fraud, and to detect patterns of fraud that may not otherwise be identified.
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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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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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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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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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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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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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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.
