├── .gitignore ├── .idea └── sqldialects.xml ├── README.md ├── binary_classification ├── README.md ├── _assets │ ├── grafana_dashboard_drift.png │ ├── grafana_dashboard_performance.png │ ├── grafana_dashboards_list.png │ └── grafana_login.png ├── data │ ├── synthetic_sample_analysis.csv │ ├── synthetic_sample_analysis_gt.csv │ └── synthetic_sample_reference.csv ├── db │ └── ddl.sql ├── docker-compose.yml ├── grafana │ └── provisioning │ │ ├── dashboards │ │ ├── NannyML │ │ │ ├── nannyml_drift.json │ │ │ └── nannyml_performance.json │ │ └── dashboards.yml │ │ └── datasources │ │ └── datasources.yml ├── nannyml │ └── config │ │ └── nann.yml └── store │ └── .gitkeep ├── blogs ├── .DS_Store └── Harnessing the Power of AWS SageMaker & NannyML │ ├── PART 0 - Creating training, testing and production datasets.ipynb │ ├── PART 1 - Training and Deploying an XGboost Model on AWS SageMaker.ipynb │ ├── prod.csv │ ├── test.csv │ └── train.csv ├── multiclass_classification ├── README.md ├── _assets │ ├── grafana_dashboard_drift.png │ ├── grafana_dashboard_performance.png │ ├── grafana_dashboards_list.png │ └── grafana_login.png ├── data │ ├── mc_analysis.csv │ ├── mc_analysis_gt.csv │ └── mc_reference.csv ├── db │ └── ddl.sql ├── docker-compose.yml ├── grafana │ └── provisioning │ │ ├── dashboards │ │ ├── NannyML │ │ │ ├── nannyml_drift.json │ │ │ └── nannyml_performance.json │ │ └── dashboards.yml │ │ └── datasources │ │ └── datasources.yml └── nannyml │ └── config │ └── nann.yml ├── regression ├── README.md ├── _assets │ ├── grafana_dashboard_drift.png │ ├── grafana_dashboard_performance.png │ ├── grafana_dashboards_list.png │ └── grafana_login.png ├── data │ ├── regression_synthetic_analysis.csv │ ├── regression_synthetic_analysis_targets.csv │ └── regression_synthetic_reference.csv ├── db │ └── ddl.sql ├── docker-compose.yml ├── grafana │ └── provisioning │ │ ├── dashboards │ │ ├── NannyML │ │ │ ├── nannyml_drift.json │ │ │ └── nannyml_performance.json │ │ └── dashboards.yml │ │ └── datasources │ │ └── datasources.yml ├── nannyml │ └── config │ │ └── nann.yml └── store │ └── .gitkeep ├── regression_incremental ├── README.md ├── _assets │ ├── grafana_dashboard_drift_1.png │ ├── grafana_dashboard_drift_2.png │ ├── grafana_dashboard_performance_1.png │ ├── grafana_dashboard_performance_2.png │ ├── grafana_dashboards_list.png │ └── grafana_login.png ├── data │ ├── regression_synthetic_analysis_with_partial_targets.csv │ └── regression_synthetic_reference.csv ├── db │ └── ddl.sql ├── docker-compose.yml ├── grafana │ └── provisioning │ │ ├── dashboards │ │ ├── NannyML │ │ │ ├── nannyml_drift.json │ │ │ └── nannyml_performance.json │ │ └── dashboards.yml │ │ └── datasources │ │ └── datasources.yml ├── incrementor │ ├── Dockerfile │ ├── incrementor.py │ └── requirements.txt ├── nannyml │ └── config │ │ └── nann.yml └── store │ └── .gitkeep └── webinars ├── 230119 - Analyzing your model's performance in production ├── Monitoring ML models in production from a notebook.ipynb └── environment.yml ├── 230124 - Monitoring in production └── 230124 NannyML webinar monitoring in production.pdf ├── 230201 - How to estimate the ML performance of deployed models ├── Performance estimation classification.ipynb ├── Performance estimation regression.ipynb ├── bike sharing.arff └── electricity-normalized.arff ├── 230309 - How to detect drift and resolve issues in your ML models ├── Drift detection.ipynb └── bike sharing.arff ├── 230330 - Deep dive into univariate drift detection methods └── Univariate methods.ipynb └── inforshare_workshop ├── README.md ├── Workshop.ipynb ├── Workshop_infoshare_NannyML_full.pptx ├── Workshop_infoshare_NannyML_full.pptx.pdf ├── medicalcov-2015_2016_2017_2018-IL-analysis.csv ├── medicalcov-2015_2016_2017_2018-IL-analysis_targets.csv └── medicalcov-2015_2016_2017_2018-IL-reference.csv /.gitignore: -------------------------------------------------------------------------------- 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