Future of Probabilistic Programming and Frameworks

How can you use Bayesian tools and optimize your models in industry? What are the best ways to communicate and visualize your models with non-technical and executive people? And what are the most common pitfalls?

In this episode, Colin Carroll will tell us how he did all that in finance and the airline industry. He’ll also share with us what the future of probabilistic programming looks like to him.

You already heard from Colin two weeks ago — so, if you didn’t catch this episode, go back in your feed’s history and enjoy the first part! 

As a reminder, Colin is a machine learning researcher and software engineer who’s notably worked on modeling risk in the airline industry and building NLP-powered search infrastructure for finance. He’s also an active contributor to open source, particularly to the popular PyMC3 and ArviZ libraries.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/!

Links from the show:

•     Colin's blog: https://colindcarroll.com/
•     Gelman’s putting model in PyMC3: https://github.com/pymc-devs/pymc3/blob/master/docs/source/notebooks/putting_workflow.ipynb
•     Matthew Kay’s quantile dotplots: https://github.com/mjskay/when-ish-is-my-bus/blob/master/quantile-dotplots.md
•     Jax, Composable transformations of Python+NumPy programs: https://github.com/google/jax
•     NumPyro, Probabilistic programming with NumPy: https://github.com/pyro-ppl/numpyro
•     Pyro, Deep Universal Probabilistic Programming: https://pyro.ai/
•     Rainier, Bayesian inference in Scala: https://github.com/stripe/rainier

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