Amazon Redshift ML Is Now Generally Available – Use SQL to Create Machine Learning Models and Make Predictions from Your Data
Amazon Redshift ML Is Now Generally Available – Use SQL to Create Machine Learning Models and Make Predictions from Your Data | Amazon Web Services
“With Amazon Redshift, you can use SQL to query and combine exabytes of structured and semi-structured data across your data warehouse, operational databases, and data lake. Now that AQUA (Advanced Query Accelerator) is generally available, you can improve the performance of your queries by up to 10 times with no additional costs and no code changes. In fact, Amazon Redshift provides up to three times better price/performance than other cloud data warehouses.
But what if you want to go a step further and process this data to train machine learning (ML) models and use these models to generate insights from data in your warehouse? For example, to implement use cases such as forecasting revenue, predicting customer churn, and detecting anomalies? In the past, you would need to export the training data from Amazon Redshift to an Amazon Simple Storage Service (Amazon S3) bucket, and then configure and start a machine learning training process (for example, using Amazon SageMaker). This process required many different skills and usually more than one person to complete. Can we make it easier?..”