Tecton and Redis team up to improve real-time ML services

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While machine learning (ML) enables a variety of use cases and can automate decisions at scale, moving it to production levels is complicated. The biggest challenges were long feedback loops and a lack of fast, continuous movement.

But modern operational ML applications simply demand more. “Many machine learning use cases require the ability to transform streaming data, provide functions to the machine learning model, and compute function values, all on a real-time basis,” said Kevin Petrie, vice president of research at Eckerson Group.

To facilitate this process for modern enterprises, real-time data platform Redis and enterprise feature store company Tecton have announced a partnership and product integration. The result, according to the two companies, is a low-latency, high-volume, highly scalable and lower-cost feature for real-time ML.

As noted by Redis Chief Business Development Officer Taimur Rashid, feature stores are the center of modern data architecture, and more organizations are storing features for low latency.

“As more organizations operationalize machine learning for real-time, performance becomes especially important for customer-facing applications and experiences,” he said. He emphasized the combination of Tecton’s data orchestration capabilities with the speed and low cost of the Redis Enterprise Cloud. “Organizations can deliver online predictions and perform complex operations in milliseconds,” he said.

The Redis Enterprise Cloud is a database-as-a-service (DBaaS) that is available in both hybrid and multicloud. It is built to support Amazon Web Services (AWS), Microsoft Azure and Google Cloud. The in-memory data store enables organizations to process, analyze, predict, and act on data in real-time with less than a millisecond latency for modern online stores.

Competitive offerings include DataStax Enterprise, Cloudera Enterprise Data Hub, MarkLogic, Couchbase, and Databricks Lakehouse Platform.

MLops brings new features for data teams

Tecton is an MLops offering that serves as a home for commonly used functions. Data teams can build new features for a project and add them to a store so they can be reused. Those features can be shared between models and use cases without the need to build data pipelines.

Feature store repositories are increasingly being used to build AI models more efficiently, and leading players in the space include Molecula, Hopsworks, and Splice Machine.

The new Tecton-Redis integration allows Tecton customers to use Redis Enterprise Cloud as an online store. According to Gaetan (GC) Castelein, Tecton’s vice president of marketing, the result delivers three times faster latencies at a cost 14 times lower than Amazon DynamoDB.

Organizations can support more demanding ML use cases, such as real-time pricing and inventory tracking or search ranking and recommendations. The new integration can also be applied to real-time fraud protection.

“We’re talking about millisecond latencies, but it’s important,” Castelein says. “Organizations must be able to detect fraud in time while providing good customer experiences.”

As he noted, Tecton customers with latency-sensitive and large-scale applications have been asking for the ability to use Redis Enterprise Cloud for their online stores for some time now. Now officially offering that option will make the company’s feature store “more flexible and modular,” he said.

“Tecton and Redis are teaming up to reduce time to action for enterprises,” said Petrie. “Tecton helps transform incoming data and calculate function values, and Redis helps retrieve ultra-low latency function values ​​for model serving.”

Castelein also underlined the benefits of speed and efficiency and the ability to offer greater integration and choice. Companies can use resulting predictions to power new apps and automate more business processes.

“ML is the future for many companies,” he said. “The goal is to make offerings as pluggable as possible, as interoperable as possible. The bottom line is that customers get more choice.”

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This post Tecton and Redis team up to improve real-time ML services

was original published at “https://venturebeat.com/2022/03/11/tecton-and-redis-partner-to-improve-real-time-ml-services/”