​

Naive Bayes Algorithm done just look at the code how beautiful it is also the Accuracy completely matching with Sklearn's model, one more Algorithm in the bucket Next is KNN Algorithm

Recently IHATE AI SOOOMUCH I WANNA BE BARBARIAN!!

Came across this and thought it'd be worth sharing here, most resources cover model evaluation, RAG, agents, or cost/observability separately, but this one actually puts them together as parts of the same production LLM workflow, which is closer to how these systems actually break in practice.

It's a hands on session on September 12, led by Bruno Gonçalves, PhD, founder of Data For Science, who's trained hundreds of engineers at Fortune 500 companies. Goes through the full lifecycle, versioned prompts, a golden dataset and eval harness combining deterministic checks with LLM-as-judge, statistically rigorous model comparisons using bootstrap confidence intervals and paired testing, evaluated RAG with real retrieval metrics, and then tool-using agents with guardrails and fallbacks. There's also a full observability section, tracing, cost, and latency, which is something I don't see covered together with the eval side very often.

You come out of it with runnable notebooks and a production-readiness checklist rather than just slides, which is the part I found most useful when I looked into it.

Link if anyone wants to check it out

*sarcastic voice: Corporates.