Q: How does the solution handle real-time and batch inference options?
A: Users have two options for provisioning the solution's pipeline: API calls to an Amazon API Gateway, or Git commits to a Git repository. In both options, the user specifies a configuration parameter for real-time or batch inference.
For real-time inferences, the pipeline creates an Amazon Sagemaker model and endpoint, then connects it to an Amazon API Gateway endpoint. The user then can call that API to run real-time inference jobs against the deployed model.
For batch inferences, the user provides an extra pipeline parameter that indicates the location of the batch inference data. When the pipeline is finished provisioning, it creates an Amazon SageMaker model, then runs the batch inference data against the created model.
Q: How does the solution support integration with third-party CI/CD deployment tools?
A: Users can provision a pipeline using API calls to Amazon API Gateway which enables them to integrate their CI/CD tools with the solution's framework. Users can operate the pipeline from the third-party CI/CD tool as long as the stages in the CI/CD tool are able to make HTTP API calls to AWS MLOps Framework.
For example, if a user's CI/CD source code contains build, test, and deploy stages, they can make an HTTP API call to the AWS MLOps Framework solution to provision a pipeline and deploy their machine learning model.
Q: What are the feedback mechanisms for a model in production?
A: After the model is deployed, it supports a range of features such as model monitoring, drift detection, and custom integration tests through Amazon Sagemaker Endpoint.
Q: Can I deploy this solution in any AWS Region?
A: No. This solution uses the AWS CodePipeline and Amazon SageMaker services,
which are not currently available in all AWS Regions. Therefore, you must launch this solution in an AWS Region where these services are available. For the most current availability by Region, refer to the AWS Service Region Table.
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