InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Excel Coding Errors Are Destroying World Economies and F# (with Tsunami) Is Here to Stop Them!
Matthew Moloney discusses using F# and .NET inside Excel, demonstrating doing big data, cloud computing, using GPGPU and compiling F# Excel UDFs.
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Creative Machines
Joseph Wilk addresses the questions if machines can be creative and what's the place of artists in such a world?
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Making Java Groovy
Ken Kousen advises Java developers how to do similar tasks in Groovy: building and testing applications, accessing both relational and NoSQL databases, accessing web services, and more.
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From The Lab To The Factory: Building A Production Machine Learning Infrastructure
Josh Wills discusses using Hadoop technologies to build real-time data analysis models with a focus on strategies for data integration, large-scale machine learning, and experimentation.
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Data Science for Hire Ed
Gloria Lau describes some of the products built for the higher education sector, the data standardization process, determining school similarity and identifying notable alumni.
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Machine Learning & Recommender Systems at Netflix Scale
Xavier Amatriain discusses the machine learning algorithms and architecture behind Netflix' recommender systems, offline experiments and online A/B testing.
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R for Big Data
Indrajit Roy presents HP Labs’ attempts at scaling R to efficiently perform distributed machine learning and graph processing on industrial-scale data sets.
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Deploying Machine Learning and Data Science at Scale
Nick Kolegraff discusses common problems and architecture to support all the phases of data science and how to start a data science initiative, sharing lessons from Accenture, Best Buy, and Rackspace.
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Working with Databases and Groovy
Paul King presents working with databases in Groovy, covering datasets, GMongo, Neo4J, raw JDBC, Groovy-SQL, CRUD, Hibernate, caching, Spring Data technologies, etc.
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Functional Programming for Optimization Problems with City of Palo Alto Open Data
Paco Nathan reviews an example data analysis application written in Cascalog used for a recommender system based on City of Palo Alto Open Data.
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Add ALL the Things: Abstract Algebra Meets Analytics
Avi Bryant discusses how the laws of group theory provide a useful codification of the practical lessons of building efficient distributed and real-time aggregation systems.
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Big Data Platform as a Service at Netflix
Jeff Magnusson details some of Netflix' key services: Franklin, Sting and Lipstick.
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