Machine learning is about your data and deployment, not just model development (sponsored by IBM)

Rob Craft shares some of the ways machine learning is being used inside of Google, explores cloud-based neural networks, and discusses some customer use cases.

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.

Not all data science problems are big data problems. Lots of small and medium product companies want to start their journey to become data driven. Nischal HP and Raghotham Sripadraj share their experience building data science platforms for various enterprises, with an emphasis on making the right architecture choices and using distributed and fault-tolerant tools.

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.

Sparklyr makes it easy and practical to analyze big data with Ryou can filter and aggregate Spark DataFrames to bring data into R for analysis and visualization and use R to orchestrate distributed machine learning in Spark using Spark ML and H2O SparkingWater. Edgar Ruiz walks you through these features and demonstrates how to use sparklyr to create R functions that access the full Spark API.

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).