Which tool allows data analysts to create and apply machine learning models using SQL commands in data warehouses?

The correct answer is Amazon Redshift ML because this tool specifically enables data analysts to create and apply machine learning models directly using SQL commands within the Amazon Redshift data warehouse environment. Amazon Redshift ML leverages built-in machine learning capabilities that seamlessly integrate with SQL, allowing users to generate models for predictions without needing to switch to a programming environment or require extensive machine learning expertise.

With Amazon Redshift ML, you can use familiar SQL syntax to run machine learning workflows, making it accessible for analysts who are well-versed in SQL. This integration is particularly beneficial because it maximizes the use of existing data stored in Redshift and enables the execution of machine learning tasks efficiently.

In contrast, while Amazon S3 is a storage service for data, it does not provide machine learning capabilities directly. Amazon Redshift is the underlying data warehouse but does not offer built-in machine learning features without Redshift ML. Amazon SageMaker is a comprehensive machine learning service that provides tools for building, training, and deploying models but operates separately from Redshift and requires more in-depth knowledge of machine learning practices.