Redis Labs introduces Landmark Machine Learning Module for Redis: Redi-ML

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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.


An Introduction to Redis-ML (Part 6) - DZone AI

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In previous posts, we learned how to use and scikit-learn to build a real-time classification and regression engine, how to use linear regression to predict housing prices, and how to use decision trees to predict survival rates. We even took a small detour into R to demonstrate ML toolkit independence, but one question we haven't focused on is, Why? Why would we want to use Redis for a real-time predictive engine? If we look at the landscape of machine learning toolkits, most focus on the learning side of ML, leaving the problem of a predictive engine to the reader. This is where Redis fills a gap; instead of trying to build a custom server, developers can rely on a familiar, full-featured data store to build their applications.


Redis module speeds Spark-powered machine learning

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In-memory data store Redis recently acquired a module architecture to expand functionality. The latest module is a machine learning add-on that accelerates delivery of results from trained data rather than training itself. Redis-ML, or the Redis Module for Machine Learning, comes courtesy of the commercial outfit that drives Redis development, Redis Labs. It speeds the execution of machine learning models while still allowing those models to be trained in familiar ways. Redis works as an in-memory cache backed by disk storage, and its creators claim machine learning models can be executed orders of magnitude more quickly with it.


Redis module speeds Spark-powered machine learning

#artificialintelligence

In-memory data store Redis recently acquired a module architecture to expand functionality. The latest module is a machine learning add-on that accelerates delivery of results from trained data rather than training itself. Redis-ML, or the Redis Module for Machine Learning, comes courtesy of the commercial outfit that drives Redis development, Redis Labs. It speeds the execution of machine learning models while still allowing those models to be trained in familiar ways. Redis works as an in-memory cache backed by disk storage, and its creators claim machine learning models can be executed orders of magnitude more quickly with it.


An Introduction to Redis-ML. Part One Redis Labs

@machinelearnbot

Despite widespread interest in machine learning (ML), using it effectively in a real-time environment is a complex problem that hasn't been given enough attention by framework developers. Nearly every language has a framework to implement the "learning" part of machine learning, but very few frameworks support the "predict" side of machine learning.