DrivenData blog
Explore the latest in our mission to build a better world using data science and AI.
Content list
Easier Code Reviews for Jupyter Notebooks with nbautoexport
Learn how DrivenData uses nbautoexport, an open-source tool we developed, to make Jupyter Notebook code reviews easier.
Community Spotlight: Quy Nguyen
The Community Spotlight features fantastic members from our DrivenData community. Quy Nguyen, a data scientist from Vietnam, talks about building time series models with lag features and the importance of storytelling in data science.
Community Spotlight: Will Koehrsen
The Community Spotlight features fantastic members from our DrivenData community. Will Koehrsen, a full-stack data scientist from central Illinois, discusses tackling climate change with data science, being self-taught, and his favorite data science tools.
America's Next Top (Statistical) Model 2020 - Benchmark
There's a lot at stake in the 2020 U.S. election. Can you predict the outcome? This benchmark shows you how to try using pollster data from FiveThirtyEight.
Meet the winners of the Clog Loss Challenge for Alzheimer's Research
Meet the winners who were best able to detect clogged blood vessels and advance Alzheimer's research from 3D image stacks.
Genetic Engineering Attribution - Benchmark
In this post we'll show you how to train a random forest model to predict the lab-of-origin from DNA sequences!
Community Spotlight: Cecil Rivers, Jr.
The Community Spotlight features fantastic members from our DrivenData community. Cecil Rivers, an electrical engineer in West Hartford, USA, talks about tricks for image processing, recruiting for diversity in the sciences, and his favorite sci-fi reads.
Community Spotlight: Lamia Zaghloul
The Community Spotlight features fantastic members from our DrivenData community. Lamia Zaghloul, a data analysis consultant in Marseille, France, discusses fog nets, generative art, and using data science competitions to "study up" the rich and powerful.
The missing guide to AzureML, Part 3: Connecting to data and running your machine learning pipeline
Use what you've learned about AzureML to create and run your first machine learning pipeline in the AzureML cloud.
The missing guide to AzureML, Part 2: Configuring your compute script and compute target
Configure cloud hardware and software to run your machine learning code.
The missing guide to AzureML, Part 1: Setting up your AzureML workspace
Get acquainted with core Azure and AzureML concepts, and set up your first AzureML workspace.
Advance Alzheimer’s Research with Stall Catchers - Benchmark
In this guest post by MathWorks, we'll show you how to start working with videos to determine if a blood vessel is stalled or flowing.
How to build a multimodal deep learning model to detect hateful memes
We're launching a new competition to detect hateful memes. In this post, we'll show you how to get started!
Meet the winners of the Open Cities AI Challenge
Meet the winners with the best building segmentation models and ethical perspectives for disaster risk management
Flu Shot Learning: Predict H1N1 and Seasonal Flu Vaccines - Benchmark
In this post, we will show you how to get started with machine learning for predicting flu vaccination.
Meet the winners of the Hakuna Ma-Data Challenge
Meet the winners who built the best wildlife identifiers!
Meet the winners of the Open AI Caribbean Challenge
Meet the winners who were best able to map disaster risk from aerial imagery!
Segmenting Buildings for Disaster Resilience - Benchmark
We'll show you how to start detecting building outlines for our latest disaster resilience challenge.
How to Use Deep Learning to Identify Wildlife
We show you how to get off to a wild start on our animal identification competition using a neural network!
Mapping Disaster Risk from Aerial Imagery - Benchmark
In this post we'll show you how to start working with aerial imagery to classify the roof material of buildings in St. Lucia, Guatemala, and Colombia.
Richter's Predictor - Benchmark
In this post we'll show you how to start using data science to predict the extent of earthquake damage!
Our Minimum Viable Process for changing and deploying software
Over the years, we have gradually settled on a process for changing and releasing software that is robust enough to prevent common failure modes but lightweight enough to be minimally annoying. Here’s what it looks like when we make a change.
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