— Insight —
This is a great essay that keeps showing up in my Inbox. It explores how the success of an idea is dependent on the software and hardware that's available. That may seem obvious but it's particularly important in domains where research is often siloed, like machine learning.
— Tools and Techniques —
Sarah Nöckel, an investment manager at Northzone, explores the latest in data tooling software. Covers data pipelines, data catalogs, data collaboration, data quality and more.
Eugene Yan's write-up of the recent RecSys conference is a great introduction to some of the latest thinking about recommender systems. This is a well organized post that covers a lot of ground and includes links to worthwhile resources along the way.
TensorFlow Recommenders is an open-source package that makes building, evaluating, and serving recommender models easy. It helps with the entire workflow of building a recommender system and aims to do that while being easy to work with and learn.
Gentle introduction to modern effect estimation.
Nubank has seen tremendous growth since launching 7 years ago. With more than 25 million customers, it's considered to be one of the largest fintechs in the world. This post offers an inside look into how Nubank manages data quality using a governance framework.
- Microprediction - Tap into the collective intelligence of community contributed time series algorithms, or add to the intelligence.
- Hivemind - Python library to train large neural networks across the internet. For instance, train one huge transformer on thousands of computers from universities, companies, and volunteers.
- modelstore - new Python library for versioning, exporting, and storing machine learning models.
- ipygany - a Project Jupyter widget for Scientific Visualization and 3D data analysis.
— Data Viz —
The U.S. recently passed 200,000 confirmed COVID-19 deaths. To help put that in perspective, this data visualization shows what that would mean if all those deaths had happened near you. If you don't live in the U.S., enter a city you may be familiar with (e.g. "Seattle, WA").
— Career —
Interviewing? Here are some key things to watch out for.