Tools posts

Explaining the basic concepts and best practices for creating Python packages in early 2023 using pyproject.toml build standards.

Code execution competitions allow participants to run their code on unseen test sets and have their best scores displayed on a live leaderboard. Read on to learn more about the what, why, and how of DrivenData code execution competitions!

Learn how DrivenData uses nbautoexport, an open-source tool we developed, to make Jupyter Notebook code reviews easier.

Use what you've learned about AzureML to create and run your first machine learning pipeline in the AzureML cloud.

Configure cloud hardware and software to run your machine learning code.

Get acquainted with core Azure and AzureML concepts, and set up your first AzureML workspace.

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.

Continuous integration is a best practice for software development. We think it's a best practice for data science, too.

Complying with open source licenses is difficult. Today we're talking about a tool that has made it easier for the Concept to Clinic challenge.

Ensuring easy setup of the application stack and efficient segmentation of the various components of the Concept to Clinic challenge is precisely where Docker excels, especially when used with Docker Compose, its tool for declaring and orchestrating multiple services. In this post, we'll dive into some background on Docker and explain how we're using it in the Concept to Clinic challenge.