Recommendation Systems (RecSys) predict what items a user is most likely to engage with, buy, or value, then rank and surface those items from a large catalog. They typically combine signals from user behavior, item attributes, and context using methods like collaborative filtering, content-based models, and deep learning–based ranking. Modern RecSys are end-to-end pipelines that ingest logs, build features and embeddings, train candidate generators and rankers, and continuously evaluate and update models in production.

Managed AutoML platforms package feature engineering, model selection, training, deployment, and monitoring into a guided workflow so teams can ship predictive models quickly without owning a full bespoke ML stack.

Simulation-Optimization combines computational simulation models with optimization algorithms to find optimal decisions under uncertainty and complex constraints. It runs many simulation scenarios to evaluate candidate solutions, using techniques like genetic algorithms, Bayesian optimization, or reinforcement learning.