Docker is great, but most tutorials are geared toward devOps users, not data scientists. If you’re building long-running processes for NLP, ML, or generative AI, here’s a blueprint for Python Docker containers for data science!

🎉 I just celebrated my first year as a junior developer. In this post, I’ll share some of the things I’ve learned and techniques I’ve grown to value over the last twelve months.

Dataframes are a powerful data structure for data processing, analytics, and ML. For many years, Pandas has been my go-to. But it can really slow you down when you’re working with big or high-dimensional data. Enter Polars!

Most of us got into data science because it’s exciting (if chaotic) and there’s a constant stream of new ideas, which is thrilling (if intimidating). But if learning is all about keeping up, why can’t our models do it?

Real-time ML models continually learn on new data as soon as it arrives, so they’re less susceptible to concept drift and data drift. Read on to learn how to use River and Vowpal Wabbit to build real-time models in Python.

Thinking about using a large language model (LLM) at your organization? Check out this tutorial to see how to bootstrap an MVP using an open source pre-trained model from HuggingFace and a free Ensign account.

It can be frustrating as a developer to wait for a large test suite to run, particularly when you have to run the suite multiple times in development. In this post, we’ll explore parallel and short modes with Go tests in an effort to …

A few weeks ago, I’d never used an event stream before. Read on to hear how I built my first event-driven data science app — the biggest challenges, my lessons learned, and a couple of key takeaways!

If you can’t deploy your models, you might feel frustrated, but you aren’t alone — only 1 in 10 data science projects ever gets deployed. The fix? We need to shift our mental model towards “thinking in events.”