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- Transcript: https://github.com/data-umbrella/event-transcripts/blob/main/2023/93-alejandro-model-risk.md
- Meetup Event: https://www.meetup.com/data-umbrella/events/296945489/
- Video: https://youtu.be/fx34LLGh2lI
- GitHub repo:
- Transcriber: ? [needs a transcriber]
- Slides and Colab: https://sites.google.com/adao.tech/portal/blog/blog_data_umbrella_model_risk_management
This is a presentation that aims to explain some of model risk management best practices to data science (split of models between input, methodology and output & model validation). The following Python libraries will be covered:
- ydata-profiling
- pycaret
- altair
ydata-profiling: is a leading package for data profiling, that automates and standardizes the generation of detailed reports, complete with statistics and visualizations.(https://docs.profiling.ydata.ai/4.6/)
pycaret: PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that exponentially speeds up the experiment cycle and makes you more productive. PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks, such as scikit-learn, XGBoost, LightGBM, CatBoost, spaCy, Optuna, Hyperopt, Ray, and a few more.(https://pycaret.org/)
altair: Vega-Altair is a declarative visualization library for Python. Its simple, friendly and consistent API, built on top of the powerful Vega-Lite grammar, empowers you to spend less time writing code and more time exploring your data.(https://altair-viz.github.io/)
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Alejandro Gomez is a mathematician, who has worked in the model risk management space for over 10 years. He is the founder of ADAO, a start up brining technology to SMEs.
- GitHub: https://github.com/agomezh
- X: https://twitter.com/a_gomez_h
- LinkedIn: https://www.linkedin.com/in/agomezh
#opensource #modelrisk
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