Improvement in item selection ratio
Highly agile and scalable
The client is a provider of subscription-based food delivery services and a customer-first platform that is headquartered in New York, United States. They wanted to develop a Contextual Bandit Recommendation Engine in the Daily Harvest GCP infrastructure to suggest the best recommendation model from some given contextual features about the user and the available N recommendation models. This would increase the chance of the user adding to the cart at least one of the recommendations by the selected recommendation model (and potentially minimize the chances of an add-to-cart that leads to pause and cancellation of subscription).
Quantiphi developed the contextual bandit solution by using epsilon greedy with deep learning neural network and the MLOps pipeline Model. Furthermore, model training was done using Vertex AI and epsilon greedy logic was implemented in FastAPI code.
“Quantiphi’s ability to balance our requirements with their deep knowledge of GCP best practices enabled us to quickly gain confidence that we were building a robust, scalable system”James Kim, Director, Data Engineering, Daily Harvest