InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Startup ML: Bootstrapping a Fraud Detection System
Michael Manapat talks about how to choose, train, and evaluate models, how to bridge the gap between training and production systems, and avoiding pitfalls.
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Real-time Stream Computing & Analytics @Uber
Sudhir Tonse discusses using stream processing at Uber: indexing and querying of geospatial data, aggregation and computing of streaming data, extracting patterns, TimeSeries analyses and predictions.
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Stream Processing with Apache Flink
Robert Metzger provides an overview of the Apache Flink internals and its streaming-first philosophy, as well as the programming APIs.
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Artificial Intelligence that Plays Atari Video Games: How Did Deep Mind Do It?
Kristjan Korjus discusses deep learning, reinforcement learning and their combination called deep Q-Network.
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Flying Faster with Heron
Karthik Ramasamy presents the design and implementation of Heron, the new de facto stream data processing engine at Twitter. Ramasamy shares Twitter’s experience of running Heron in production.
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Rethinking Streaming Analytics for Scale
Helena Edelson addresses new architectures emerging for large scale streaming analytics based on Spark, Mesos, Akka, Cassandra and Kafka (SMACK) or Apache Flink or GearPump.
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Connecting Stream Processors to Databases
Gian Merlino discusses stream processors and a common use case - keeping databases up to date-, the challenges they present, with examples from Kafka, Storm, Samza, Druid, and others.
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Real-Time Fraud Detection with Graphs
Jim Webber talks about several kinds of fraud common in financial services and how each decomposes into a straightforward graph use-case. He explores them using Neo4j and Cypher query language.
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Insights from History of Rock Music via Machine Learning
Ali Kheyrollahi uses clustering and network analysis algorithms to analyze the publicly available Wiki data on rock music to find mathematical relationship between artists, trends and subgenres.
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NET Machine Learning: F# and Accord.NET
Alena Hall presents various machine learning algorithms available in Accord.NET - a framework for machine learning and scientific computing in .NET.
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High Performance Stream Processing
S Maldini, G Renfro and D Turanski dissect a Spring XD app to show design patterns and techniques for getting the highest throughput and lowest resource utilization in streaming apps.
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Developing Real-time Data Pipelines with Apache Kafka
Joe Stein makes an introduction for developers about why and how to use Apache Kafka. Apache Kafka is a publish-subscribe messaging system rethought of as a distributed commit log.
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