Jan 18, 2017 · post

New Research on Probabilistic Programming


We’re excited to release the latest research from our machine intelligence R&D team! 

This report and prototype explore probabilistic programming, an emerging programming paradigm that makes it easier to construct and fit Bayesian inference models in code. It’s advanced statistics, simplified for data scientists looking to build models fast.

Bayesian inference has been popular in scientific research for a long time. The statistical technique allows us to encode expert knowledge into a model by stating prior beliefs about what we think our data looks like. These prior beliefs are then updated in light of new data, providing not one prediction, but a full distribution of likely answers with baked-in confidence rates. This allows us to asses the risk of our decisions with more nuance.

Bayesian methods lack widespread commercial use because they’re tough to implement. But probabilistic programming reduces what used to take months of thorny statistical sampling into an afternoon of work.

This will further expand the utility of machine learning. Bayesian models aren’t black boxes, a criterion for regulated industries like healthcare. Unlike deep learning networks, they don’t require large, clean data sets or large amounts of GPU processing power to deliver results. And they bridge human knowledge with data, which may lead to breakthroughs in areas as diverse as anomaly detection and music analysis

Our work on probabilistic programming includes two prototypes and a report that teaches you:

  • How Bayesian inference works and where it’s useful
  • Why probabilistic programming is becoming possible now
  • When to use probabilistic programming and what the code looks like
  • What tools and languages exist today and how they compare
  • Which vendors offer probabilistic programming products

Finally, as in all our research, we predict where this technology is going, and applications for which it will be useful in the next couple of years.

Probabilistic Real Estate Prototype

One powerful feature of probabilistic programming is the ability to build hierarchical models, which allow us to group observations together and learn from their similarities. This is practical in contexts like user segmentation: individual users often shares tastes with other users of the same sex, age group, or location, and hierarchical models provide more accurate predictions about individuals by leveraging learnings from the group.


We explored using probabilistic programming for hierarchical models in our Probabilistic Real Estate prototype. This prototype predicts future real estate prices across the New York City boroughs. It enables you to input your budget (say $1.6 million) and shows you the probability of finding properties in that price range across different neighborhoods and future time periods.

Hierarchical models helped make predictions in neighborhoods with sparse pricing data. In our model, we declared that apartments are in neighborhoods and neighborhoods are in boroughs; on average, apartments in one neighborhood are more similar to others in the same location than elsewhere. By modeling this way, we could learn about the West Village not only from the West Village, but also from the East Village and Brooklyn. That means, with little data about the West Village, we could use data from the East Village to fill in the gaps! 

Many companies suffer from imperfect, incomplete data. These types of inferences can be invaluable to improve predictions based on real-world dependencies.

Play around with the prototype! You’ll see how the color gradients give you an intuitive sense for what probability distributions look like in practice.

Read more

Jan 25, 2017 · interview
Jan 11, 2017 · interview

Latest posts

Nov 15, 2022 · newsletter

CFFL November Newsletter

November 2022 Perhaps November conjures thoughts of holiday feasts and festivities, but for us, it’s the perfect time to chew the fat about machine learning! Make room on your plate for a peek behind the scenes into our current research on harnessing synthetic image generation to improve classification tasks. And, as usual, we reflect on our favorite reads of the month. New Research! In the first half of this year, we focused on natural language processing with our Text Style Transfer blog series. more
Nov 14, 2022 · post

Implementing CycleGAN

by Michael Gallaspy · Introduction This post documents the first part of a research effort to quantify the impact of synthetic data augmentation in training a deep learning model for detecting manufacturing defects on steel surfaces. We chose to generate synthetic data using CycleGAN,1 an architecture involving several networks that jointly learn a mapping between two image domains from unpaired examples (I’ll elaborate below). Research from recent years has demonstrated improvement on tasks like defect detection2 and image segmentation3 by augmenting real image data sets with synthetic data, since deep learning algorithms require massive amounts of data, and data collection can easily become a bottleneck. more
Oct 20, 2022 · newsletter

CFFL October Newsletter

October 2022 We’ve got another action-packed newsletter for October! Highlights this month include the re-release of a classic CFFL research report, an example-heavy tutorial on Dask for distributed ML, and our picks for the best reads of the month. Open Data Science Conference Cloudera Fast Forward Labs will be at ODSC West near San Fransisco on November 1st-3rd, 2022! If you’ll be in the Bay Area, don’t miss Andrew and Melanie who will be presenting our recent research on Neutralizing Subjectivity Bias with HuggingFace Transformers. more
Sep 21, 2022 · newsletter

CFFL September Newsletter

September 2022 Welcome to the September edition of the Cloudera Fast Forward Labs newsletter. This month we’re talking about ethics and we have all kinds of goodies to share including the final installment of our Text Style Transfer series and a couple of offerings from our newest research engineer. Throw in some choice must-reads and an ASR demo, and you’ve got yourself an action-packed newsletter! New Research! Ethical Considerations When Designing an NLG System In the final post of our blog series on Text Style Transfer, we discuss some ethical considerations when working with natural language generation systems, and describe the design of our prototype application: Exploring Intelligent Writing Assistance. more
Sep 8, 2022 · post

Thought experiment: Human-centric machine learning for comic book creation

by Michael Gallaspy · This post has a companion piece: Ethics Sheet for AI-assisted Comic Book Art Generation I want to make a comic book. Actually, I want to make tools for making comic books. See, the problem is, I can’t draw too good. I mean, I’m working on it. Check out these self portraits drawn 6 months apart: Left: “Sad Face”. February 2022. Right: “Eyyyy”. August 2022. But I have a long way to go until my illustrations would be considered professional quality, notwithstanding the time it would take me to develop the many other skills needed for making comic books. more
Aug 18, 2022 · newsletter

CFFL August Newsletter

August 2022 Welcome to the August edition of the Cloudera Fast Forward Labs newsletter. This month we’re thrilled to introduce a new member of the FFL team, share TWO new applied machine learning prototypes we’ve built, and, as always, offer up some intriguing reads. New Research Engineer! If you’re a regular reader of our newsletter, you likely noticed that we’ve been searching for new research engineers to join the Cloudera Fast Forward Labs team. more

Popular posts

Oct 30, 2019 · newsletter
Exciting Applications of Graph Neural Networks
Nov 14, 2018 · post
Federated learning: distributed machine learning with data locality and privacy
Apr 10, 2018 · post
PyTorch for Recommenders 101
Oct 4, 2017 · post
First Look: Using Three.js for 2D Data Visualization
Aug 22, 2016 · whitepaper
Under the Hood of the Variational Autoencoder (in Prose and Code)
Feb 24, 2016 · post
"Hello world" in Keras (or, Scikit-learn versus Keras)


In-depth guides to specific machine learning capabilities


Machine learning prototypes and interactive notebooks

ASR with Whisper

Explore the capabilities of OpenAI's Whisper for automatic speech recognition by creating your own voice recordings!


A usable library for question answering on large datasets.

Explain BERT for Question Answering Models

Tensorflow 2.0 notebook to explain and visualize a HuggingFace BERT for Question Answering model.

NLP for Question Answering

Ongoing posts and code documenting the process of building a question answering model.

Cloudera Fast Forward Labs

Making the recently possible useful.

Cloudera Fast Forward Labs is an applied machine learning research group. Our mission is to empower enterprise data science practitioners to apply emergent academic research to production machine learning use cases in practical and socially responsible ways, while also driving innovation through the Cloudera ecosystem. Our team brings thoughtful, creative, and diverse perspectives to deeply researched work. In this way, we strive to help organizations make the most of their ML investment as well as educate and inspire the broader machine learning and data science community.

Cloudera   Blog   Twitter

©2022 Cloudera, Inc. All rights reserved.