This Guidance uses Amazon AppFlow, a fully managed integration service that helps you securely transfer data between different services. This Guidance also uses SageMaker Data Wrangler, SageMaker Autopilot, and cloud-native support and integration for Amazon S3, facilitating the preparation and transformation of your dataset. With SageMaker Autopilot, you can retrain and deploy your model with updated datasets as needed.

This Guidance requires an AWS Identity and Access Management (IAM) account, which restricts access and permissions to the minimum required permissions for the service to function. Additionally, this Guidance has server-side encryption through either Amazon S3 or AWS Key Management Service (AWS KMS).

This Guidance supports durable storage through Amazon S3 and automatic scaling through SageMaker. You can also monitor SageMaker through CloudWatch, which converts raw data into readable metrics in near real time and sets alarms for when you reach thresholds.

This Guidance uses SageMaker Autopilot, which can generate notebooks to manage multiple automatic-ML jobs and experiments. You can edit these notebooks as needed, and features like explainability help you better understand the model.

This Guidance uses serverless services such as Lambda and services that scale to match demand, such as SageMaker Autopilot, Amazon RDS, Amazon Redshift, and Amazon S3, so you only pay for the resources you need. You can also choose between on-demand pricing, a savings plan, or a combination of the two for further cost savings.

This Guidance uses Amazon SageMaker Model Monitor, which can automate your model drift detection, thereby reducing resource usage.