Announcing general availability of Amazon Athena ML powered by Amazon SageMaker
Today, Amazon Athena announced the general availability of a new capability that makes working with machine learning models as simple as running a SQL query. You can now build and deploy machine learning models in Amazon SageMaker and use SQL functions in Amazon Athena to generate predictions from your SageMaker models. This enables analytics teams to make model-driven insights available to business users and analysts without the need for specialized tools and infrastructure.
Today, Amazon Athena announced the general availability of a new capability that makes working with machine learning models as simple as running a SQL query. You can now build and deploy machine learning models in Amazon SageMaker and use SQL functions in Amazon Athena to generate predictions from your SageMaker models. This enables analytics teams to make model-driven insights available to business users and analysts without the need for specialized tools and infrastructure.
Amazon SageMaker is a fully managed service that helps you prepare, build, train, and deploy high quality machine learning models. To generate inferences from a previously trained SageMaker model, Amazon Athena users first register the model and its inputs using Athena SQL functions. When the query is executed, model inferences are generated and returned to the user in real-time and with low latency. Users can leverage their Amazon SageMaker models using the Amazon Athena console, SDK, and JDBC and ODBC drivers.
To get started, see the following resources:
- To learn more about this functionality, see Using Machine Learning (ML) with Amazon Athena
- To learn more about how to build machine learning models, see Build, train, and deploy a machine learning model with Amazon SageMaker