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Alejandro Gomez: Model Risk Management Best Practices for Data Science

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About the Event

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/)

## Timestamps
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#92

About the Speaker

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.

#opensource #modelrisk

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Model Risk Management Best Practices for Data Science

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