Machine learning at Google (sponsored by Google)

Cesar Berho and Alan Ross offer an overview of open source project Apache Spot (incubating), which delivers next-generation cybersecurity analytics architecture through unsupervised learning using machine-learning techniques at cloud scale for anomaly detection.

Ted Dunning offers an overview of tensor computingcovering, in practical terms, the high-level principles behind tensor computing systemsand explains how it can be put to good use in a variety of settings beyond training deep neural networks (the most common use case).

Unbounded, out-of-order, global-scale data is now the norm. Even for the same computation, each use case entails its own balance between completeness, latency, and cost. Kenneth Knowles shows how Apache Beam gives you control over this balance in a unified programming model that is portable to any Beam runner, including Apache Spark, Apache Flink, and Google Cloud Dataflow.

Eric Frenkiel explains how to use real-time data as a vehicle for operationalizing machine-learning models by leveraging MemSQL, exploring advanced tools, including TensorFlow, Apache Spark, and Apache Kafka, and compelling use cases demonstrating the power of machine learning to effect positive change.

Teradata joined the Presto community in 2015 and is now a leading contributor to this open source SQL engine, originally created by Facebook. Join Kamil Bajda-Pawlikowski to learn about Presto, Teradata's recent enhancements in query performance, security integrations, and ANSI SQL coverage, and its roadmap for 2017 and beyond.

Food production and preparation have always been labor and capital intensive, but with the internet of things, low-cost sensors, cloud-computing ubiquity, and big data analysis, farmers and chefs are being replaced with connected, big data robotsnot just in the field but also in your kitchen. Tim Gasper explores the tech stack, data science techniques, and use cases driving this revolution.