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Public datasets can help you improve the accuracy of your machine-learning models. Use curated datasets to save time on data preparation and discovery. These datasets are ready for use in machine-learning workflows and can be accessed from Azure services. Consider real-world factors which can have an impact on business outcomes. By incorporating features of curated datasets in your machine learning model, you can improve the accuracy and reduce the time required for data preparation. Share datasets with the growing community of data analysts and developers. Azure Open Datasets can be used to deliver insights at hyperscale with Azure's machine-learning and data analytics solutions. Open Datasets are free to use. Open Datasets are free to use, but you'll only be charged for the Azure services you consume, such as virtual machines, storage, networking resources and machine learning. Open data that has been curated and made available on Azure.

Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case. Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection. Vertex AI Agent Builder empowers developers to design and deploy advanced generative AI applications for enterprise use. It supports both no-code and code-driven development, enabling users to create AI agents through natural language prompts or by integrating with frameworks like LangChain and LlamaIndex.