<p>In this episode, we explore how Instacart leverages machine learning to suggest smart replacements for out-of-stock products — a challenge that’s central to the grocery delivery experience. We dive into Instacart’s two-model approach, where a deep learning model uncovers general product relationships across the catalog, and an engagement model learns from customer behavior to personalize those recommendations. Together, they power a system that makes replacements more accurate, relevant, and efficient at scale.</p><p>For more details, you can refer to their published tech blog, linked here for your reference: <a href="https://tech.instacart.com/how-instacart-uses-machine-learning-to-suggest-replacements-for-out-of-stock-products-8f80d03bb5af"><u>https://tech.instacart.com/how-instacart-uses-machine-learning-to-suggest-replacements-for-out-of-stock-products-8f80d03bb5af</u></a> </p>