AWS Launches Amazon Redshift Ml to Deploy Machine Learning (Ml) Models Using Familiar SQL Commands
Amazon | December 09, 2020
Amazon Redshift ML makes it feasible for information distribution center clients, for example, information experts, data set engineers, and information researchers to make, train, and send AI (ML) models utilizing natural SQL orders. Amazon Redshift is the most generally utilized cloud information distribution center and, with Amazon Redshift ML, you would now be able to use Amazon SageMaker, a completely overseen AI administration, utilizing SQL and without moving your information or learning new abilities.
With Amazon Redshift ML fueled by Amazon SageMaker, you can utilize SQL proclamations to make and prepare AI models from your information in Amazon Redshift and afterward utilize these models for use cases, for example, beat forecast and misrepresentation hazard scoring straightforwardly in your questions and reports. Amazon Redshift ML naturally finds and tunes the best model dependent on the preparation information utilizing Amazon SageMaker Autopilot. SageMaker Autopilot picks among the best relapse, parallel, or multi-class grouping and direct models.
On the other hand, you can pick a model kind, for example, Xtreme Gradient Boosted tree (XGBoost), a difficult sort like relapse or grouping, and preprocessors or hyperparameters. Amazon Redshift ML utilizes your boundaries to construct, train, and send the model in the Amazon Redshift information distribution center. You can acquire forecasts from these prepared models utilizing SQL inquiries as though you were summoning a client characterized work (UDF) and influence all advantages of Amazon Redshift, including greatly equal handling abilities.
Amazon Redshift ML use your current bunch assets for forecast so you can stay away from extra Amazon Redshift charges. There is no extra Amazon Redshift charge for making or utilizing a model, and forecast happens locally in your Amazon Redshift group, so you don't need to pay extra except if you need to resize your bunch. Amazon Redshift ML utilizes Amazon SageMaker for preparing your model, which has an extra related expense. View the Redshift estimating page for subtleties.
The Redshift ML review is accessible in the accompanying districts: US East (Ohio), US East (N Virginia), US West (Oregon), US West (San Francisco), Canada (Central), Europe (Frankfurt), Europe (Ireland), Europe(London), Europe (Paris), Europe (Stockholm), Asia Pacific (Hong Kong) Asia Pacific (Tokyo), Asia Pacific (Singapore), Asia Pacific (Sydney), and South America (São Paulo).