How to Build and Ship Real-Time Recommendation Systems Faster

An Engineering Guide and Blueprint

Real-time recommendation systems are a critical driver of user engagement and revenue for digital products. But engineering teams can spend up to 24 months just building the underlying intelligence infrastructure, which requires stitching together Kafka streams to feature stores, maintaining complex stream-processing pipelines, and managing low-latency APIs before they start seeing results.

Download the guide to get a technical blueprint so you can ship in weeks, not months or years.

What You’ll Learn

• Why the underlying infrastructure is so difficult to build and maintain
• The core components of real-time intelligence infrastructure
• How to accelerate recommendation system delivery
• A technical ML recommendations blueprint with example use cases

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