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Real-Time Retail Intelligence: Building E-Commerce Recommendations with Lakebase and AI Search on Databricks

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  • A complete reference architecture for building a multi-stage recommendation and ranking engine on Databricks — from clickstream ingestion to real-time personalized serving — replacing fragmented ML infrastructure with a single unified platform.
  • The system delivers low latency personalized product recommendations by combining pre-computed batch recommendations with a real-time scoring path, using Databricks AI Search for candidate retrieval, Lakebase for online feature serving, and Model Serving for low-latency inference.
  • By unifying data engineering, feature management, model training, and real-time serving on one governed platform, e-commerce teams eliminate glue code, accelerate iteration cycles, and gain end-to-end lineage from raw clickstream to production predictions.

    • The opportunity: Personalization as a revenue engine

    Every second a shopper spends on a fashion e-commerce app generates a stream of intent signals — searches, product views, wishlist additions, cart interactions. The platforms that convert those signals into relevant product recommendations in real time are the ones that win. Industry benchmarks show that effective personalization can lift conversion rates by 10–30% and increase average order value significantly.

    Yet building a production-grade recommendation system remains one of the hardest ML engineering challenges. It demands real-time data ingestion, complex feature engineering, multiple ML models working in concert, and serving infrastructure that responds in milliseconds — all while keeping inventory, location, and business rules in sync.

    This blog presents a complete reference architecture for building such a system on Databricks, based on a real-world implementation for a leading fashion e-commerce platform in Asia serving over 1 million monthly active users across a catalog of 100,000+ SKUs.

    System overview: The architecture at a glance

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    Read the original at the source: https://www.databricks.com/blog/real-time-retail-intelligence-building-e-commerce-recommendations-lakebase-and-ai-search

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    Databricks — imported from official source
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    October 02, 2026 12:00
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    https://www.databricks.com/blog/real-time-retail-intelligence-building-e-commerce-recom...

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