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If you already use SageMaker for hosting models, you can still make use of the powerful features of DataRobot, including AutoML and time series modeling -- you can integrate DataRobot into your existing deployment processes. Likewise, you can use this to deploy a DataRobot-built model in another environment.
About this Accelerator
In this accelerator we are going to take an ML model that has been built and refined within DataRobot and deploy it to run within AWS SageMaker.
What you will learn
- Programmatically go through the end-to-end steps of building a model with DataRobot
- Export and host the model in AWS SageMaker
- To help with the setup of AWS services to run the model, this code will also help provision any extra items that you may not haven yet set up:
AWS
- ECR Repository
- S3 Bucket
- IAM Role for SageMaker
- SageMaker inference model
- SageMaker endpoint configuration
- SageMaker endpoint (for real time predictions)
- SageMaker batch transform job (for batch predictions)
DataRobot
- DataRobot AutoML Project
- DataRobot AutoML Models
- Scoring Code JAR file of AutoML Model
Additional Resources
If you found this tutorial helpful and would like to learn more about DataRobot and AWS SageMaker, we encourage you to check out our docs website and explore the many other features and capabilities of these platforms. Whether you are a data scientist, machine learning engineer, or developer, there is something for everyone to learn and leverage in these powerful tools.
