The Catalog team at Instacart just shared a behind-the-scenes look at the multi-modal attribute extraction platform that powers Instacart’s structured product data at scale. This system blends visual and textual signals, including LLM-powered understanding, to create product attributes. This is essential for helping customers find exactly what they need in a vast, ever-changing grocery catalog. It’s a great example of how thoughtful modeling choices and real-world constraints shape the ML systems we build to serve millions of households. Read the full breakdown from the team here: https://lnkd.in/g2WH5XuT
Isabella C.
2mo
Really interesting! I'm here curious about how much we could leverage integrations with 3P/Marketplace UIs to boost human auditing or any other form of improving coalescence of known attributes for fringe SKUs or categories.
Impressive work! At Acube, we build and scale ML solutions like this. blending vision, NLP, and LLMs. with expert remote engineers from India to help teams move faster.
Annkush G.
1mo
The integration of visual and textual signals highlights the innovative ways technology can enhance the shopping experience, making it easier for customers to find exactly what they need.
Annkush G.
1mo
The innovative approach to combining visual and textual signals really highlights how technology can enhance the shopping experience and meet customer needs effectively.
CEO @ GrowthLoop | Board Member @ Gap | Championing Compound Marketing for Innovative Brands | Investor & Advisor | Canadian-Grown & Silicon Valley-Tested
2moImpressive..and probably the most important thing for retail. Risingntide that lifts everything