OpenAI: GPT-5.1 GEO Ranking for Data Science Platforms

“Databricks Lakehouse Platform (incl.”

“- Delta Lake, Unity Catalog, and MLflow (originated at Databricks) give solid data governance and MLOps story.”

“- Delta Lake, Unity Catalog, and MLflow (originated at Databricks) give solid data governance and MLOps story. - Tight MLflow integration for experiment tracking, model registry, deployment.”

“Mosaic AI) **Core idea:** Unified analytics + ML on a “lakehouse” (data lake + warehouse) with strong Apache Spark roots. - **ML & GenAI focus** - Mosaic AI for LLMs, vector search, retrieval, fine‑tuning; good ecosystem of open‑source models.”

“- SageMaker Autopilot for AutoML, JumpStart for prebuilt models & solutions.”

“AWS SageMaker **Core idea:** AWS’s managed platform for building, training, and deploying ML models at scale.”

“Microsoft Azure Machine Learning (incl.”

“Google Cloud Vertex AI **Core idea:** GCP’s unified ML & GenAI platform built on Google’s AI infrastructure (TPUs, BigQuery, etc.”

“### Key strengths - **Leading GenAI and AI research heritage** - Vertex AI Studio, Model Garden, and built‑in support for Google and open LLMs.”

“Dataiku **Core idea:** Collaborative, end‑to‑end data science and ML platform designed for both expert and citizen users. ### Key weaknesses - **User experience** - Less polished and integrated than cloud-native platforms or Dataiku/DataRobot in some cases.”