Automating Machine Learning Testing using GitHub Actions and DeepChecks
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Updated
Jul 16, 2024 - Python
Automating Machine Learning Testing using GitHub Actions and DeepChecks
Repository contains the detail about ML model deployment and building end-to-end ML pipeline for production
Modelling and prediction of default + deployment via AWS Sagemaker
Comparison between several Python data profile libraries.
A production-grade MLOps pipeline for image classification — featuring ZenML orchestration, MLflow tracking, DeepChecks validation, and automated model deployment.
End-to-end MLOps pipeline for Telco Customer Churn. Hydra + DVC + MLflow + XGBoost (hyperopt-tuned with scale_pos_weight) + BentoML 1.2 service + GitHub Actions deploying to GHCR. Test-set-leakage-free training, 38 tests (Pandera + pytest-steps + Deepchecks + HTTP integration), all 8 phases adversarially reviewed by Opus.
Credit risk analytics project for loan default ranking with Python, XGBoost, SHAP, threshold strategy, monitoring, and governance.
Deep learning-driven cache eviction policy using LSTM forecasting and uncertainty estimation, deployed as a production microservice with a full MLOps pipeline.
Independent SR 11-7-style model validation of an ML trading strategy - deepchecks + giskard, formal report, reproducible. Verdict: not approved for deployment.
"End-to-end MLOps pipeline for image classification using ZenML, MLflow, and TensorFlow. Features automated training, continuous deployment, drift detection with Deepchecks, and a Streamlit frontend."
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