How Wrong Is Your Model?
In this blog, we look at the topic of uncertainty quantification for machine learning and deep learning. By no means is this a…
Beyond LDA: State-of-the-art Topic Models With BigARTM
This post follows up on the series of posts in Topic Modeling for text analytics. Previously, we looked at the LDA (Latent Dirichlet…
Are GPUs Really Expensive? Benchmarking GPUs for Inference on Databricks Clusters
It is no secret that GPUs are critical for artificial intelligence and deep learning applications since their highly-efficient architectures make them ideal for…
GPU-accelerated Sentiment Analysis Using Pytorch and Huggingface on Databricks
Sentiment analysis is commonly used to analyze the sentiment present within a body of text, which could range from a review, an email…
MLflow for Bayesian Experiment Tracking
This post is the third in a series on Bayesian inference ([1], [2] ). Here we will illustrate how to use managed MLflow…
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…
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…
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.…
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…
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…
