Agentic Machine Learning Modeling

Instacart is exploring agent-assisted machine learning modeling loops to accelerate the development and iteration of ML models. This approach involves human MLEs defining problems and supervising agents, which are tasked with generating hypotheses, coding solutions, and evaluating them. The system leverages Instacart's in-house ML platform, Griffin, for model estimation and evaluation, and is developing new infrastructure specifically for agentic modeling use cases. Case studies in delivery time and catalog attribute extraction demonstrate improvements in model performance (e.g., 3-5% offline MAE reduction for delivery time, significant recall gains for attribute extraction) and reduced iteration time. The process involves adaptive hypothesis generation, model family exploration, hyperparameter tuning, and combining smaller improvements, mimicking human MLE workflows.