This Guidance is represented by two AWS Cloud Development Kit (AWS CDK) stacks. You can deploy changes to the application and infrastructure by applying the best practices from AWS CDKs. Programmatic access key, single sign-on, or federation are some of the authentication methods used in the AWS CDK CLI. 

An Amazon CloudWatch dashboard provides business metrics for monitoring. You can configure CloudWatch alarms to meet your operational needs.

To improve privacy and security, Amazon Virtual Private Cloud (Amazon VPC) service endpoints are used. Managed services such as AWS Lambda and Amazon ECS are used to reduce the security maintenance tasks. Amazon S3 buckets used in this Guidance are encrypted and blocked from public access. Amazon Elastic Block Store (Amazon EBS) volumes are encrypted using the customer managed AWS Key Management Service (AWS KMS) key.

This solution uses managed services Amazon EMR, SageMaker, and Amazon ECS to minimize the operational efforts for the solutions. Amazon EMR is designed to handle large-scale data processing. The model training is done on SageMaker that provides the configuration parameters to size the infrastructure according to the demand. The data processing and model training parts of this solution are run periodically in batches. The inference part must be near real-time and run as containers in Amazon ECS, allowing for multi-Availability Zone deployment and auto-scaling to enable high availability.

For a hands-on experience, you can deploy the provided code in your account from an AWS Region of your choice. You would first use the sample data to start the data processing and machine learning process and then use a trained model to make inferences. To start tailoring this Guidance to your needs, you can incorporate your own data, modify the machine learning model training, and possibly adjust the inference part.

This Guidance is designed to support managed services that can run in any AWS region without incurring additional licensing costs. Deploy to the AWS Region that best fits your needs. The components are managed individually to incur cost only when used. Data transformation uses Amazon EMR clusters to provide the best cost to performance ratio when processing large data sets. From SageMaker studio, clusters can be created and ended to manage costs. The inference component uses container-based deployment to match resource consumption with actual demand.

This Guidance focuses on shared storage and single sources of truth to avoid data duplication and reduce the total storage requirements of your workload. Only fetch data from shared storage if necessary. Detach unused volumes in order to make more resources available. You can change the utilized compute resources based on the actual need. Adjust provisioned resources based on the actual demand. To understand utilization and the right-size of deployed resources, you can use CloudWatch metrics.