Feb. 8, 2024, 8 a.m. | Rafal Gancarz

InfoQ - AI, ML & Data Engineering www.infoq.com

Instacart combined machine learning with event-based processing to create an architecture that provides customers with an indication of item availability in near real-time. The new solution helped to improve user satisfaction and retention by reducing order cancellations due to out-of-stock items. The team also created a multi-model experimentation framework to help enhance model quality.

By Rafal Gancarz

ai apache flink apache kafka architecture architecture & design availability customers data warehousing development event event-driven-architecture experimentation framework instacart kinesis machine machine learning ml & data engineering mlops near postgres processing real-time retention s3 solution sql stock team

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