Rule-based systems and machine learning aren't as different as people make them sound.

I started Module 1 of #MLZoomcamp by Alexey Grigorev this week, and two things clicked for me.

First: a rule-based system is code you write that produces an outcome. You look at spam emails, notice patterns, and hardcode them: sender is X, subject contains Y, mark as spam. Works fine, until spam changes and you're adding another rule. Then another. Six months in, it's a pile of exceptions nobody wants to touch.

Machine learning flips that relationship. Instead of you writing the logic, you already have the outcomes, emails people manually marked as spam, and you feed those in. The algorithm works out the logic itself. The rules you already wrote aren't wasted work either. They become your starting features.

Second thing that clicked: ML projects follow a process called CRISP-DM. Understand the problem, get the data, prepare it, train a model, evaluate it, deploy it, repeat.

Reading through it, I realized I already do a version of this without naming it. Checking whether data is reliable enough before building on it. Cleaning and reshaping raw data into something usable. Going back and revising earlier assumptions once you learn something new from the data itself. That's most of what a data engineer's day looks like anyway, just applied to a different end goal.

It's easy to accidentally build a model on information it wouldn't actually have in the real world, feeding it "current" data while training on past cases. Looks great in testing, falls apart in production. Same instinct that makes you double check a backfill when the numbers look a little too clean.

One module in, and the overlap between data engineering and ML is bigger than I expected.

More nuggets as I work through the rest of the Machine Learning Zoomcamp.