H2O has announced several new advancements including improved security features, semi-supervised deep learning, and capabilities for large Java models. New features were also added for tree algorithms like gradient boosted machines to improve accuracy, and H2O will now support convolutional and recurrent neural networks for tasks like image recognition and time series analysis. Deep Water was announced as H2O's next generation deep learning platform to bring enterprise deep learning capabilities.
This document describes a predictive maintenance system for robots using real-time sensor data. A team of 4 engineers built a solution in 2 months using standard open source software like H2O and MapR. Sensors on a robot collected accelerometer, gyroscope and other data. This raw data was analyzed using anomaly detection algorithms in H2O to build a machine learning model that identified normal vs abnormal robot states. The model was deployed as a microservice to make real-time predictions on new sensor data and detect potential failures. The solution was able to analyze data from hundreds of robots and identify anomalies within 3 seconds, demonstrating an effective low-cost predictive maintenance system.
Moving at the speed of a startup often means rapid iterative development, which can lead to a patchwork of systems and processes. In the early days at Kik (one of the most popular chat apps among U.S. teens), the data team was able to move extremely quickly but often at the expense of scalable data engineering. In this session, Kik’s head of data will share the eight things they did to save time and money. The team took their data stack from a complex combination of systems and processes to a scalable, simple, and robust platform leveraging Apache Spark and Databricks to make data super easy for everyone in the company to use.
This document discusses Apache Ignite and how it can be used with Apache Spark for fast data applications. It provides an overview of Ignite's in-memory data fabric capabilities, how it compares to Spark, and how Ignite can be integrated with Spark to provide shared resilient storage and distributed computing. Examples are given of reading and writing data between Ignite and Spark and using Ignite's in-memory file system and SQL support from Spark.