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Redis for feature stores

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Get the fastest machine learning predictions

How it works

Get ultra-low-latency predictions

Real-time AI apps need immediate responses, and we deliver sub-millisecond predictions. That means we can support the most time-sensitive operations, including fraud detection, dynamic pricing, and recommendation systems where fast decisions and responses are key.

Integrate with existing tech stacks

We work seamlessly with top offline stores, like Tecton and Feast, to integrate into existing ML workflows. This streamlines data management by accessing, storing, and retrieving ML features directly and ensures smooth transitions between storage layers so apps work better and get to production faster.

Reduce downtime

Our five-nines availability and single-digit-second failover are crucial for mission-critical AI apps, delivering data consistency and uninterrupted operations, minimizing downtime, protecting data integrity, and maintaining smooth workflow continuity.

How iFood uses feature stores

“It’s not just about putting in a food order. It’s about providing an optimal experience for our customers. It’s really, really fast. Plus it’s a lot cheaper.”

Daniel Galinkin
Head of ML Engineering, iFood

<1ms

millisecond per read

Read more customer stories

Learn more about feature stores

Blog

Apr.07.2022

Feature Stores for Real-time AI/ML: Benchmarks, Architectures, and Case Studies

Read how feature stores support leading-edge machine learning use cases and how the feature store you choose can impact cost and performance.

Video

Nov.10.2022

Using Redis as Online Feature Store for Real-time Inference

Explore how to implement real-time machine learning pipelines using Feast feature store and how Redis enables low latency prediction and scoring.

Link

Nov.19.2020

Building a Gigascale ML Feature Store with Redis

Learn how DoorDash used Redis to build a large scale feature store for high throughput, batch random reads, and the constraints of low latency.

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