Turning a trained churn classifier into a FastAPI service that other software can actually call.

The post My Model Worked Perfectly. Then I Tried to Make It Useful. appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #B4] - A diagnostic and five composable operations, not a decision tree

The post Tables in PDFs for RAG: Don’t Flatten the Grid appeared first on Towards Data Science.

I built a prompt dependency graph that separates everything a component can reach from the smaller set that actually needs targeted evaluation.

The post Changing One Prompt Can Affect 50 Others — I Built a Prompt Dependency Graph to Find What Needs Retesting appeared first on Towards Data Science.

A practical framework for reducing uncertainty before agents accelerate implementation

The post How to Solve the Right Problem in the Age of Agentic AI appeared first on Towards Data Science.

I built a matcher meant to finish the cleanup that normalization left behind. Testing it against real data showed that no version of it could be made safe. What follows is the architecture that was left once the matcher was set aside.

The post Avoiding Entity Key Drift in a Data Lake: Step 2, When Fuzzy Matching Stops Working appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #B3] - A confident wrong answer is a bug. A bare “no answer” with no justification is almost as bad. Each of the four bricks has one piece of evidence to show

The post A RAG That Says “Not in This Document” Has to Show Four Kinds of Evidence 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.

Why the standard groupBy function isn’t enough

The post A Practical Introduction to PySpark Window Functions appeared first on Towards Data Science.

Five failure modes that survive constrained decoding, and why your schema validator will never catch them.

The post Your JSON Is Valid but Your Data Is Wrong: Five Failure Modes LLM Structured Outputs Won't Catch 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.

More informed decision-making through uncertainty quantification

The post Beyond Point Predictions: A Practical Introduction to Bayesian Neural Networks appeared first on Towards Data Science.

What each one solves, and runnable code you can paste into a notebook.

The post 5 AI Skills That Will Keep Data Scientists Relevant in 2027 appeared first on Towards Data Science.

What I learned after thinking more carefully about Structured Outputs on messy, incomplete data

The post Your LLM Can Return Perfect JSON and Still Be Wrong appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #B2] - The FAQ inverts every brick of the standard RAG pipeline. Parsing is trivial, retrieval doubles as a cache, and few-shot prompting becomes a retrieval problem too

The post FAQ as RAG: When You Get to Design the Corpus 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.

The five MLOps monitoring assumptions agents break, and which inherited signals now pass failed runs as healthy.

The post AgentOps Is Not MLOps: What Breaks in Your Monitoring Stack When Agents Go to Production 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.

Enterprise Document Intelligence [Vol.1 #B1] - Three sources of one problem. User typos, fast-typing transcription noise, OCR character errors. Classical spell-check handles one of them. Embeddings carry the rest

The post Noisy Text in RAG: Typos, OCR, and the Gap Classical Spell-Check Leaves appeared first on Towards Data Science.

Four skills worth adding to your workflow today if you don't want to be left behind

The post 4 Claude Skills Every Data Scientist Needs in 2026 appeared first on Towards Data Science.