[Online] Model Risk Management Best Practices for Data Science

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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How to Join the Webinar
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You can join via your browser (no app download required). Use Chrome or Firefox. Pre-register for the webinar:
https://www.bigmarker.com/neo4j/Data-Umbrella-Webinar

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Video Recording
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This event will be recorded and placed on our YouTube. We usually have it up within 24 hours of the event. Subscribe to our YT and set your notifications: https://www.youtube.com/c/DataUmbrella/

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Time
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17:00 UTC, 9pm PT / 12pm ET/ 8pm EAT/ 10:30pm IST

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Additional Details
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Talk Level: Intermediate
Pre-reqs: Beginner Python
Prep Work: None
Resource: None

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