Amazon Redshift ML Now Generally Available
Amazon Redshift ML is now available to anyone and can be used to build, train, and deploy machine learning models directly from an Amazon Redshift cluster.
Redshift is Amazon’s cloud-based data warehouse service at the petabyte scale. Redshift was originally based on ParAccel technology from Actian (formerly known as Ingres), which Amazon acquired in 2013.
Redshift data can be analyzed using standard SQL-based tools and business intelligence applications. Queries can be distributed and parallelized across multiple nodes, and Amazon has automated most of the common administrative tasks associated with managing data warehouses. Amazon also offers Advanced Query Accelerator (AQUA) for Amazon Redshift, a distributed, hardware-accelerated cache that Amazon says means Redshift can run up to ten times faster than any other cloud data warehouse by performing a substantial share. data processing on site. on its hardware-accelerated cache.
The new machine learning tools follow the “easy date” principle. To create a machine learning model, you use an SQL query to specify the data that you want to use to train your model and the output value that you want to predict.
After running the SQL command to build the model, Redshift ML exports the specified data from Amazon Redshift to your S3 bucket and calls Amazon SageMaker Autopilot to prepare the data. SageMaker is a fully managed service for the machine learning process. It includes a web-based IDE for comprehensive machine learning workflows, designed to allow developers to build, train, tune, and deploy their models from a single interface. Redshift ML uses SageMaker for feature preprocessing and engineering. You then select the appropriate predefined algorithm and apply the algorithm for training the model. You can optionally specify the algorithm to use, for example XGBoost.
Redshift ML manages all interactions between Amazon Redshift, S3, and SageMaker, including all steps involved in training and compilation. After the model is trained, Redshift ML uses Amazon SageMaker Neo to optimize the model for deployment and makes it available as an SQL function. You can then use the SQL function to apply the machine learning model to your data in queries, reports, and dashboards.
Redshift ML is available now.
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