Amazon Redshift now supports linear learner algorithm with Redshift ML
Amazon Redshift ML enables you to create, train, and deploy machine learning (ML) models using familiar SQL commands. With Amazon Redshift ML, you can leverage Amazon SageMaker, a fully managed machine learning service, without moving your data or learning new skills. Amazon Redshift now supports Amazon SageMaker Linear Learner algorithm for creating models with Amazon Redshift ML.
Amazon Redshift ML enables you to create, train, and deploy machine learning (ML) models using familiar SQL commands. With Amazon Redshift ML, you can leverage Amazon SageMaker, a fully managed machine learning service, without moving your data or learning new skills. Amazon Redshift now supports Amazon SageMaker Linear Learner algorithm for creating models with Amazon Redshift ML.
You can use linear learner algorithms for supervised training for either linear or logistic regression problems for use cases such as predicting sales of a product, determining marketing effectiveness, or to predict customer’s willingness to purchase a product or service. You can specify model_type as LINEAR_LEARNER with CREATE MODEL command to create a ML model with Linear Learner.
Linear Learner model is available in all AWS commercial regions where Amazon Redshift ML is available. See AWS Region Table for more details. You can find more information about Linear Learner algorithm from the Redshift developers guide or this blog.