As online grocery business accelerated in 2020, Instacart search, which supports one of the largest catalog of grocery items in the world, started facing new challenges. We experienced a sudden surge in the number of users, retailers and traffic to our search engine. As a result, the scale of our data grew manifold and the predictive performance of our model started degrading due to lack of historical data for many new retailers and users that started using Instacart. New users searched for queries that we have never seen before. The new retailers on our platform were quite diverse - ranging from local grocery stores to office supplies, pharmacies and halloween stores - which are categories that our models were never trained on. As our relatively small team team of four engineers tried to build new models to address these issues, we faced a number of operational challenges. This talk will focus on details about the challenges we encountered in this new world including drift in our data and cold start issues. We will cover the architecture of our search engine and the issues we faced in training and serving our ML models due to the increase in scale. We will talk about how we we overcame the issues by using more sophisticated models that are trained and served on a more robust infrastructure and technical stack. We will also cover the iterations on our ML ranking models to adapt to this new world and we successfully improved the quality of search results and our revenue while operating in a robust production environment.

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