Generative AI is a family of models that learn the statistical structure of data (text, images, audio, code, etc.) and then sample from that learned distribution to create new content. These models are typically built with deep neural architectures such as transformers, diffusion models, and GANs, and can be conditioned on prompts, examples, or structured inputs. In applications, generative models are often combined with retrieval systems, tools, and business logic to ground outputs in real data and workflows. Effective use requires careful attention to safety, reliability, governance, and alignment with domain constraints.

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.

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.