The last few years have seen a massive influx of “data scientists” who complete a few MOOCs, participate in a Kaggle competition or two, and think that experience is representative of life as a data scientist in the enterprise. If that mindset persists, and they show no desire to change it, they are what I call rogue data scientists.

Go distroless and reduce the image size as well as the number of CVEs for machine learning containers.

Check out the latest industry research for enterprise machine learning.

How many machine learning platforms run on Kubernetes? Which machine learning platforms can run in air-gapped environments? How common are feature stores in current machine learning platforms?

What is all the fuss about feature stores in machine learning?

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’.