Compression vs Cryptography: What Comes First?
Data encryption and compression are heavyweight algorithms that must be used with care in performance intensive applications; but when applying both mechanics to the same data, which should come first?
Oct 31, 2023Welcome to Ensign U!
Like any new tool, Ensign takes a little time to learn. But take it from us (a bunch of seasoned data science experts) – it will open up a new world of data science applications for you. Check out this beginner-friendly introduction!
Oct 20, 2023Spooky `asyncio` Errors and How to Fix Them
You’ve heard the
asyncio
library unlocks concurrency for Python with minimal syntactical overhead, but the terminology makes you tremble! Don’t panic โ here are 3 of the most common errors you will encounter and how to fix them.Oct 13, 2023Rotational Announces Partnership, Hackathon with the University of Chicago Data Science Institute
Rotational Labs is excited to announce our partnership with the University of Chicago Data Science Institute, including co-hosting a hackathon to help students learn to apply real-time data science techniques to real-world problems.
Sep 27, 2023How to Dockerize Python Data Science Processes
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!
Sep 14, 2023My First Year as a Junior Developer at Rotational Labs
๐ 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.
Sep 1, 2023Intro to Polars: A Pandas Alternative for Efficiently Working with Large Datasets
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!
Aug 22, 2023What is Incremental Machine Learning?
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?
Aug 21, 2023Using River and Vowpal Wabbit to Build Real-Time Machine Learning Models
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.
Aug 11, 2023