by Angela Guess

According to a new press release, “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.”

The release goes on, “Redis-ML enriches Spark ML in the following areas: (1) Faster Prediction Generation: Storing and serving your trained Spark Machine Learning models directly from Redis, parallelizes access to the models and significantly improves performance. Initial benchmarks showed 5x to 10x latency improvement over the standard Spark solution in real time classifications. (2) Redis-ML avoids the need to generate the model from file systems or other disk based data stores, a process which usually involves long serialization/deserialization overheads with slow disk accesses. With Redis-ML, at the end of the training phase, the model is just stored in its native format in Redis. (3) Consistent Prediction Delivery: As user traffic grows, it is important to guarantee real-time recommendations and predictions at a consistent speed to the end user. With Redis-ML, recommendations and predictions are delivered at consistent speed no matter how many concurrent users are accessing the model.”

Read more at Marketwired.

Photo credit: Redis Labs