Ann: Results of First Court Hearing, page-33

Spot on - What you are referring to is Machine Learning Operations. It's unfortunate that the model performed poorly in the validation study. It could have been overtraining, changes in the dominant strains' sound signature, or the prominence of other respiratory diseases. Regardless, changing settings is something that machine learning algorithms need to deal with. In fact, these changing settings is what makes a machine learning approach superior to over methods of diagnosis. The following describes machine learning operations for those who are interested;


"Machine Learning Operations (MLOps) technology and practices provide a scalable and governed means to deploy and manage machine learning models in production environments.MLOps provides critical capabilities to enable machine learning in production, including:

1. Simplified model deployment. Data scientists use a variety of modeling languages, frameworks, and tools. With MLOps, IT operations teams can quickly deploy models from a variety of languages and frameworks in production environments.

2. Monitoring for machine learning. Tools for monitoring software do not work for machine learning. MLOps provides monitoring designed for machine learning. Key capabilities include data drift detection for important features and model-specific metrics.

3. Production life cycle management. The initial model deployment is the beginning of a long life cycle of updates to keep a machine learning model running. MLOps provides a means to test and update models in production without interrupting service to business applications.

4. Production model governance. Machine learning models used in production applications will need to be tightly controlled to prevent unwanted changes and to comply with regulations. MLOps provides access control, traceability, and audit trails to minimize risk and ensure regulatory compliance."

https://www.datarobot.com/wiki/machine-learning-operations-mlops/