Swiggy Improves Search Autocomplete Using Real Time Machine Learning Ranking

1. 1Build a candidate generation system using OpenSearch
2. 2Implement a feature store for real-time signals
3. 3Apply learning to rank models for improved relevance
4. 4Configure the system to maintain strict latency constraints
5. 5Test the system with continuous model updates

Data scientists and engineers on a team can benefit from this approach to improve search autocomplete functionality, while product managers can understand the potential impact on user experience

💡 Separating candidate generation and ranking, and using feature stores for real-time signals, can improve search autocomplete relevance while maintaining low latency

🚀 Swiggy improves search autocomplete with real-time ML ranking! 🤖