A practitioner's guide to estimating what an opt-in AI feature actually did, when nobody randomized it.
The post Your AI Adoption Lift Is a Selection Effect appeared first on Towards Data Science.
Why AI coding makes software design more important
The post Software Design in the Age of AI appeared first on Towards Data Science.
Frequentist confidence intervals and Bayesian credible intervals answer different questions, and confusing them can distort product decisions
The post The 95% Illusion: Why Your Confidence Interval Isn't What You Think It Is appeared first on Towards Data Science.
Why autonomous agents expose a new explainability problem in fraud detection
The post What SHAP Can't Explain About Agentic AI Fraud appeared first on Towards Data Science.
A model is only as reliable as the assumptions behind it
The post Who Questions What Works: When Should We Retest Our Assumptions? appeared first on Towards Data Science.
Understanding permutation symmetry in deep learning, and what it means for weight averaging and model merging
The post The Symmetry That Breaks Neural Network Averaging appeared first on Towards Data Science.
How we split a tightly coupled Python pipeline into independently deployable services
The post When One Process Becomes Too Much: Splitting a Pipeline into MCP Services appeared first on Towards Data Science.
How model validation standards are changing for LLM-based systems: what breaks, what carries over, and how to test output quality
The post The Model Validation Playbook for GenAI: Lessons from Banking appeared first on Towards Data Science.
I saved my day with a mere $52 bill. But you don't have to repeat my mistake.
The post I Vibe-Coded an App in Just Two Hours (And Regretted It the Next Day) appeared first on Towards Data Science.
From scalar observations to self-attention, and how positional information restores sequence order
The post Why Transformers Need Positional Encoding For Time Series: A Visual Guide appeared first on Towards Data Science.
Why the default 50/50 split is the wrong move when your treatment is more expensive than your control, and how cost-based sampling weights fix it
The post Optimal Traffic Allocation Under Heterogeneous Variant Cost appeared first on Towards Data Science.
A visual guide to how graph neural networks work under the hood
The post Graph Neural Networks: GCN, MPNN, and GAT, Explained Simply appeared first on Towards Data Science.
The hidden assumptions behind the data we observe.
The post What We Miss About Missing Values appeared first on Towards Data Science.
A framework for building RAG pipelines that introduces complexity in response to observed failure modes, from lexical and hybrid search to reranking and agentic information seeking
The post Why RAG Complexity Should Be Earned appeared first on Towards Data Science.
Quick and simple tips to help you write better agent instructions
The post 8 Tips for Writing Effective Agent Instructions appeared first on Towards Data Science.
How to apply the latest context engineering guidelines to your day-to-day data science work
The post Context Engineering Is Changing. Here’s What It Means for Data Scientists appeared first on Towards Data Science.
As AI handles more of the execution, what work should belong to agents vs humans and why does that distinction matter?
The post Agentic AI Is Rewriting The Analytics Stack But There's One Skill It Still Can't Touch appeared first on Towards Data Science.
A practical guide to getting better code, not just more code
The post How to Work with AI Coding Agents appeared first on Towards Data Science.
Why most agents are just flowcharts in disguise, and what to build instead.
The post Is Agentic AI Just Automation? appeared first on Towards Data Science.
Watermarks act at the model’s moments of doubt, and so do the safety checks that catch AI mistakes
The post Hallucinations, Watermarks, Removers, and a Squeezed Balloon appeared first on Towards Data Science.
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