Data Science and Machine Learning Platforms provide a unified workbench for data scientists and ML engineers across the full model development lifecycle — exploratory analysis, notebook-based authoring, feature engineering, model training, experiment tracking, and hand-off to production deployment. Core capabilities include collaborative notebook environments with versioning and reproducibility, managed compute with GPU support, visual and code-based workflow authoring, AutoML and guided model development, experiment tracking and model registries, feature stores (in some platforms), and integration with downstream MLOps deployment tooling. The category spans notebook-first products, end-to-end ML platforms, and cloud-vendor ML suites. Leading vendors include Databricks (ML), Dataiku, DataRobot, H2O.ai, Hex, Deepnote, Domino Data Lab, and the cloud-native suites AWS SageMaker, Azure Machine Learning, and Google Vertex AI.