The Generalist's Guide to Machine Learning

I'm a generalist software engineer. I think I'm a generalist in most aspects of my life. I've never wanted to be pigeonholed as a front-end or a backend dev. Same for languages: I'm not a Python person or a Kotlin kid. I can't solve complete problems without knowing a little about a lot.

I always considered machine learning an exception. A domain too specialised to understand, with lots of maths and academic knowledge required. The closest I came was translating Jupyter Notebooks into applications that could run in production.

That's changed. Even when the next AI winter comes (and it'll be a cold one), people will still expect that little sprinkle of ML magic in their applications.

What Makes Code Good

I've written the coding standards at half a dozen companies by now. I generally don't care about where you put the curly braces or if else's are cuddled, I'm happy to let an opinionated formatting tools decide that for me.

For every rule, I should be able to explain why it's valid. If my explanation is weak, so is my rule.