Alibaba Platform for AI (PAI)

Next-generation SageMaker (rebranded SageMaker AI) unifying data, analytics, and AI in one workspace — Studio notebooks, HyperPod for foundation-model training at scale, Lakehouse with QuickSight + S3 Tables integration, AutoPilot AutoML, managed training jobs, hosted inference endpoints, and Feature Store, with re:Invent 2024 introducing the unified SageMaker AI workspace and 2025 Summit additions extending it with lakehouse auto-onboarding

End-to-end platform for building and deploying ML models with automated ML, designer (drag-and-drop), managed compute clusters, MLflow tracking, and responsible AI dashboards

Hosted MLflow with managed tracking server, model registry, and deployment integration — open-source ML lifecycle tooling tightly coupled to Databricks Unity Catalog and Mosaic AI

Unified platform to build, deploy, and scale ML models with AutoML, custom training on TPUs and GPUs, model registry, pipelines, feature store, and generative AI studio

End-to-end AI development platform with AutoML, data labelling, distributed training on Ascend and GPU clusters, and one-click deployment to cloud or edge

Enterprise AI studio for training, validating, tuning, and deploying foundation models and traditional ML models, with IBM's Granite model family, Hugging Face integration, prompt lab, synthetic data generation, and governance via watsonx.governance

Managed MLOps platform (formerly Open Data Hub) for training, serving, and monitoring ML models on OpenShift with JupyterHub, KServe, Kubeflow, and PyTorch operators

Fully managed machine learning platform with JupyterLab notebooks, conda-environment library, job orchestration, model deployments as HTTPS endpoints, feature store, and model catalog — integrated with Autonomous Database and Object Storage for end-to-end ML workflows