A short course for ML engineers who want to understand uncertainty, causality, and the probabilistic layer underneath the systems they already build.
Why your model's offline AUC doesn't translate online — and the causal reasoning that explains it
The 20% of probabilistic thinking that covers 80% of real ML problems, without the math pain
When and how to reach for probabilistic tools alongside your existing deep learning stack
I'm building this course now. Join the waitlist and I'll send you the first module free when it launches.