And learn to use it with one of the most popular way! One of the most valuable technology skills is the ability to analyze huge data sets, and this course is specifically designed to bring you up to speed on one of the best technologies for this task, Apache Superset! The top technology companies like Google, Facebook, Netflix, Airbnb, Amazon, NASA, and more are all using Apache Superset to solve their big data problems! What is this course about? This course covers all the fundamentals about Apache Spark Machine Learning Project with Scala and teaches you everything you need to know about developing Spark Machine Learning applications using Scala, the Machine Learning Library API for Spark.

This post is the second part in a series where we will build a real-time example for analysis and monitoring of Uber car GPS trip data. If you have not already read the first part of this series, you should read that first. The first post discussed creating a machine learning model using Apache Spark's K-means algorithm to cluster Uber data based on location. This second post will discuss using the saved K-means model with streaming data to do real-time analysis of where and when Uber cars are clustered. The example data set is Uber trip data, which you can read more about in part 1 of this series.

Data Discovery: The first phase involves analysis on historical data to build the machine learning model. Analytics Using the Model: The second phase uses the model in production on live events. Data Discovery: The first phase involves analysis on historical data to build the machine learning model. Analytics Using the Model: The second phase uses the model in production on live events. In this first post, I'll help you get started using Apache Spark's machine learning K-means algorithm to cluster Uber data based on location.

MOUNTAIN VIEW, CA--(Marketwired - Nov 1, 2016) - Today, Redis Labs, the home of Redis, introduced an open source project Redis-ML, the Redis Module for Machine Learning that accelerates the delivery of real-time recommendations and predictions for interactive apps, in combination with Spark Machine Learning (Spark ML). Machine learning is fast becoming a critical requirement for modern smart applications. Redis-ML accelerates the delivery of real-time predictive analytics for use cases such as fraud detection and risk evaluation in financial products, product or content recommendations for e-commerce applications, demand forecasting for manufacturing applications or sentiment analyses of customer engagements. Spark ML (previously MLlib) delivers proven machine learning libraries for classification and regression tasks. Combined with Redis-ML, applications can now deliver precise, re-usable machine learning models, faster and with lower execution latencies.