InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Microservices for a Streaming World
Ben Stopford discusses using stream processing tools for real-time business apps, handling infinite streams, leveraging high throughput, deploying dynamic, fault-tolerant, and streaming services.
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How Comcast Uses Data Science and ML to Improve the Customer Experience
Jan Neumann presents how Comcast uses machine learning and big data processing to facilitate search for users, for capacity planning, and predictive caching.
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The Mechanics of Testing Large Data Pipelines
Mathieu Bastian explores the mechanics of unit, integration, data and performance testing for large, complex data workflows, along with the tools for Hadoop, Pig and Spark.
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Modeling Avengers: Open Source Technology Mix for Saving the World
The speakers discuss Smart Farming System Tooling, an environment to model, analyze and simulate an agricultural exploitation, biomass growth and water consumption based on user input and open data.
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Understanding Real-time Conversations on Facebook
Janet Wiener discusses using a data pipeline and graphic visualizations to extract and analyze the Chorus – the aggregated, anonymized voice of the people communicating on Facebook - in real time.
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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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