InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Next Gen Hadoop
Akmal B. Chaudhri introduces Apache™ Hadoop® 2.0 and Yet Another Resource Negotiator (YARN).
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What Can Hadoop Do for You?
Eva Andreasson presents typical categories of problems that are commonly solved using Hadoop and also some concrete examples in each category.
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Design Patterns for Large-Scale Real-Time Learning
Sean Owen provides examples of operational analytics projects, presenting a reference architecture and algorithm design choices for a successful implementation based on his experience Oryx/Cloudera.
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Haskell in the Newsroom
Erik Hinton discusses the successes and failures of making a cultural shift in the newsroom at NYT to accept Haskell and some of the projects Haskell has been used for.
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Sync is the Future of Mobile Data
Chris Anderson provides code samples on how to build offline applications for mobile platforms based on the NoSQL document model, and how to contribute to the open source projects behind this movement
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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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