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This project adopts a modular Python architecture within an MLOps framework to enhance subscription renewal predictions, utilizing FastAPI and MongoDB with AWS integration (S3, ECR, EC2). Docker ensures seamless deployment, and GitHub Actions automate the CI/CD workflows. Evidently AI monitors drift to guarantee predictive accuracy and reliability.
Analyses a streaming dataset of insects. Given the features of an insect the algorithm decides if it is a pest or not. Different Classification algorithms applied and their accuracies visualized. ADWIN is used as drift detection method since different insects dominate different seasons.
e2e machine learning pipeline using a config based approach for classification problems. Supports grouping and grading classifiers in addition to online learning algorithms