Bayesian models offer greater theoretical advantages compared to non-probabilistic methods, and also allow for more flexible model design. But how can one leverage these theoretical advantages to build a successful data science product?
Bayesian frameworks offer powerful theoretical advantages: they can take advantage of prior information and provide a better sense of uncertainty.
In practice however, the theoretical barrier-to-entry and complexity surrounding Bayesian methods often discourage data scientists from applying these methods in real-life contexts to build successful data products.
This talk will demonstrate how Bayesian methods can and should be used to build innovative data products. More specifically, it will show how a startup used Bayesian Hierarchical Models and PyMC3 to build a next-generation brand tracking tool. This talk is relevant for data scientists, machine learning engineers, product owners and researchers who are curious about how to leverage the advantages of Bayesian methods to add an entirely new level of value to your product.
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Abstract as a tweet:How can one leverage the power of Bayesian methods to build a successful data science product?
Domains:Algorithms, Business & Start-Ups, Data Science, Machine Learning, Science, Statistics
Domain Expertise:expert