High-level solution framework used to resolve the issue
Industrialization was undertaken for the client by extracting and converting the Jupyter Notebooks, applying OOPs concepts, hyper-parameterizing the code, transforming to server-less functions (OpenFaas) driven by NiFi Workflows, and deploying them over Azure-based MDP Platform using CI/CD.
Further, a Dash-based UI was developed to display the predictions, capture the user feedback, i.e., labels, and retrain the models. Additional Screens were also designed to display the Model Performance metrics.
Subsequently, ML and Data Pipelines were created for various stages. Training and Inference Pipelines were implemented to automate training/retraining or predictions of Anomaly Detection and Classification Models.