Benefits of BYOM
With Amazon Redshift ML, you can use an existing ML model built in Amazon SageMaker and use it in Redshift without having to retrain it. To use BYOM, you need to provide model artifacts or a SageMaker endpoint, which takes a batch of data and returns predictions. BYOM is useful in cases where a machine learning model is not yet available in Redshift ML, for example, at the time of writing this book, a Random Cut Forest model is not yet available in Redshift ML, so you can build this model in SageMaker and easily bring it to Redshift and then use it against the data stored in Redshift.
Here are some specific benefits of using Redshift ML with your own ML model:
- Improved efficiency: By using an existing ML model, you can save time and resources that would otherwise be spent on training a new model
- Easy integration: Redshift ML makes it easy to integrate your ML model into your data pipeline, allowing you to use it for real-time predictions or batch predictions...
