ML using Amazon Redshift and Amazon Athena
Many times, all the data is already processed, stored, and consumed out of Amazon Redshift using SQL-based queries. Database engineers can easily create complex SQL-based consumption patterns, but they lack the understanding to stitch together all the components of ML pipelines using SageMaker. To make their day-to-day-job lives easy, they can now build ML models inside Amazon Redshift using SQL syntax. Redshift ML handles all interactions with Amazon SageMaker, transparent to the data developer.
Some of the benefits of using Redshift ML are set out here:
- Simplicity: Makes it easy to create ML models using SQL. Even the predictions are done using SQL statements.
- Flexibility: Allows the user to select specific ML algorithms such as XGBoost. Under the covers, the best ML model is automatically trained and tuned.
- Performant: Even though under the covers the models are trained with SageMaker, they are eventually deployed in...
