Changes

As part of the suite of Conductivity, Temperature, Depth (CTD) AI tools being produced by the Office of the Chief Data Steward (OCDS), we are developing a model to assist with identifying and deleting poor-quality scans during the CTD quality control process. Using a combination of a Gaussian Mixture Model (GMM) to cluster CTD scans into groups with similar physical properties and Multi-Layer Perceptrons to classify the scans in each group, we are able to automatically flag the poor-quality scans to be deleted with a high degree of accuracy. Through the deployment of the model as a real-time online endpoint and the support of model communication through a client-side program, we have successfully integrated an experimental model into the client's business process in a field testing environment. The continuation of this line of work will now look to bring the model into a production environment for regular usage in the quality control process.

As part of the suite of Conductivity, Temperature, Depth (CTD) AI tools being produced by the Office of the Chief Data Steward (OCDS), we are developing a model to assist with identifying and deleting poor-quality scans during the CTD quality control process. Using a combination of a Gaussian Mixture Model (GMM) to cluster CTD scans into groups with similar physical properties and Multi-Layer Perceptrons to classify the scans in each group, we are able to automatically flag the poor-quality scans to be deleted with a high degree of accuracy. Through the deployment of the model as a real-time online endpoint and the support of model communication through a client-side program, we have successfully integrated an experimental model into the client's business process in a field testing environment. The continuation of this line of work will now look to bring the model into a production environment for regular usage in the quality control process.

==Next Steps ==

==Next Steps ==

Having gone through the proof-of-concept and field test stages, the model has demonstrated positive results that can lead to business value by speeding up the CTD quality control process. We are now in a position to begin preparations to bring the model into a production environment. Refinements to the model deployment and integration architecture and backing code will be implemented to ensure that the functionality can be supplied to the client as a robust tool. Monitoring must also be implemented to ensure that the model continues to deliver reliable performance over time. If model drift is detected during monitoring, this must trigger corrective action to retrain the model. Finally, an in-depth model reporting dashboard will be designed to provide detailed information on model performance, enabling the user to better understand the tool they are working with.

Having gone through the proof-of-concept and field test stages, the model has demonstrated positive results that can lead to business value by speeding up the CTD quality control process. We are now in a position to begin preparations to bring the model into a production environment. Refinements to the model deployment, integration architecture and backing code will be implemented to ensure that the model functionality can be supplied to the client in the form of a robust tool. Monitoring must also be implemented to ensure that the model continues to deliver reliable performance over time. If model drift is detected during monitoring, this must trigger corrective action to retrain the model. Finally, an in-depth model reporting dashboard will be designed to provide detailed information on model performance, enabling the user to better understand the tool they are working with.