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!

Data and machine learning technologies for the big data era have been developed in the last twenty years. Let’s review these two decades in terms of data processing and machine learning frameworks as well as machine learning platforms that have been spotted in the wild.

Let’s take a closer look at the more popular open-source tools for data engineering, machine learning, and container orchestration.

It’s a familiar situation: you’ve spent the better part of an hour crafting an email with all relevant background information and various suggestions before you hit ‘Send’. A day goes by. Then another. You wonder, “Did no one read it?” The truth is: somebody probably did, but none of the recipients were inclined to go through your magnum opus. Too bad, but not entirely unavoidable. Here is my business email etiquette guide to make it easier for recipients to reciprocate with ‘Reply’.

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.