An Experimentation Pipeline for Extracting Topics From Text Data Using PySpark
This post is part of a series of posts on topic modeling. Topic modeling is the process of extracting topics from a set of text documents. This is useful for understanding or summarizing large collections of text documents. A document can be a line of text, a paragraph or a chapter in a book. The...
Using Bayesian Hierarchical Models to Infer the Disease Parameters of COVID-19
In a previous post, we looked at how to use PyMC3 to model the disease dynamics of COVID-19. This post builds on this use case and explores how to use Bayesian hierarchical models to infer COVID-19 disease parameters and the benefits compared to a pooled or an unpooled model. We fit an SIR model to...
The Modern Chief Data Officer: Transitioning From Defense to Offense
The Chief Data Officer (CDO) is not a new position – Capital One reportedly had a CDO all the way back in 2002. But only recently has it become a mainstream, business-critical role for enterprises. In a recent study by NewVantage Partners, 65% of surveyed companies have a CDO position within their organization. And as...
Reproduce Anything: Machine Learning Meets Data Lakehouse
Machine learning has proved to add unprecedented value to organization and projects - whether that’s for accelerating innovation, personalization, demand forecasting and countless other use cases. However, machine learning (ML) leverages data from a myriad of sources with an ever-changing ecosystem of tools and dependencies, making these solutions constantly in flux and difficult to reproduce....
Bayesian Modeling of the Temporal Dynamics of COVID-19 Using PyMC3
In this post, we look at how to use PyMC3 to infer the disease parameters for COVID-19. PyMC3 is a popular probabilistic programming framework that is used for Bayesian modeling. Two popular methods to accomplish this are the Markov Chain Monte Carlo (MCMC) and Variational Inference methods. The work here looks at using the currently...
