When you work with the Google Cloud Platform, console.cloud.google.com is your home base outside of the Cloud SDK. It requires a lot of clicking, even with pinned products in the side bar. As a developer I prefer the keyboard to the mouse. So, how can you use the keyboard to boost your productivity in Google Chrome?

Python development without virtual environments is a pain; with virtual environments it quickly gets messy. Here is a shell script to make life with Python easier.

Scalafmt is a popular formatter for Scala. The formatting it produces is not always identical across versions, even with the same configuration file. To ensure all developers on a team format the code in the same way, I’ll show you how to roll your own Scalafmt action container with Docker.

Here’s my list of essential reading for people in the data and machine learning space.

Machine Learning (ML) is known as the high-interest credit card of technical debt. It is relatively easy to get started with a model that is good enough for a particular business problem, but to make that model work in a production environment that scales and can deal with messy, changing data semantics and relationships, and evolving schemas in an automated and reliable fashion, that is another matter altogether. If you’re interested in learning more about a few well-known ML platforms, you’ve come to the right place!

A common practice in hiring in the tech industry is the take-home interview challenge, which, despite its neutral appearance, is implicitly biased and can actually decrease diversity in an industry that is already predominantly young, white, and male.

There can be many reasons why employees quit. Many people leave for jobs with better remuneration at other companies, yet the companies they leave behind hire people who do the same. Therein lies a paradox: why are fewer people promoted than hired from outside, especially since those who quit obtain pay rises significantly larger than those who stay at the same company?

Lean Data and Machine Learning Operations (D/MLOps) is the adoption of the ‘lean’ philosophy from manufacturing. Its aim is to continuously improve the operation of data and machine learning pipelines.