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Build Probabilistic Machine Learning Apps Using Streamlit:
A Case Study of COVID-19 Reproduction Metric

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Rapidly build and deploy probabilistic machine learning apps using Streamlit’s open-source app framework. Make your apps easily accessible to end users who don't have Bayesian expertise and without having to go through long product-release cycles.

As a case study, we shall use PyMC3 to build a probabilistic model that estimates the reproduction metric of COVID-19. Then, we'll quickly build an app using Streamlit - all in pure Python.

WED 5/27
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Learn from our Data Science Experts How to Rapidly Build Probabilistic Machine Learning Apps

About The Hosts

Daniel Emaasit

Daniel Emaasit is a Data Scientist at Haystax. His interests involve developing principled probabilistic models for problems where training data are scarce by leveraging knowledge from subject-matter experts and context information. In particular, he is interested in flexible probabilistic machine learning methods, such as Gaussian processes and Dirichlet processes, and data-efficient learning methods such as Bayesian optimization & Model-based Reinforcement Learning.

He is the creator of Pymc-learn, a library for practical probabilistic machine learning in Python.