Beyond Point Predictions: A Practical Introduction to Bayesian Neural Networks
More informed decision-making through uncertainty quantification
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5 AI Skills That Will Keep Data Scientists Relevant in 2027
What each one solves, and runnable code you can paste into a notebook.
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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
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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
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Noisy Text in RAG: Typos, OCR, and the Gap Classical Spell-Check Leaves
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
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4 Claude Skills Every Data Scientist Needs in 2026
Four skills worth adding to your workflow today if you don't want to be left behind
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We use the equation all the time. But where did it actually come from?
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I Trained Six Models for Fraud Detection, and the Best One Isn't in Production
What a final-year project taught me about the gap between evaluation metrics and production decisions
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Bagging hits a wall no amount of trees can break — here's the equation that explains why, and the experiment that proves it
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How to Format Your TDS Draft: A New and Improved Guide
Everything you need to know about creating a draft on our Contributor Portal
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One Document Type, a Million Files: Structured Extraction into the SQL Table RAG Queries
Enterprise Document Intelligence [Vol.1 #14C] - One hour with two people, six to ten fields, and the two signals that separate a real column from one that will break a filter later
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I Deployed My Data Pipeline to AWS. Then Everything That Was “Local” Broke.
What moving a Dockerized pipeline off my laptop taught me about containers, networking, and hidden assumptions.
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10 Positions for Enterprise RAG That Mainstream Tutorials Get Wrong
Enterprise Document Intelligence [Vol.1 #M3] - The ten positions the series argues from, and the map of every article that argues them
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Survival Analysis and the Cox Proportional Hazards Model: A Beginner-Friendly Guide
From Kaplan-Meier curves to hazard ratios with runnable Python Code throughout
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Bug Detection Blind Spots in AI Coding Harnesses (GStack and Beyond)
28 debugging experiments reveal that AI struggles less with complexity than with missing information.
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Why We Fine-Tuned SigLip (And Why That’s Not Always the Right Call)
LoRA fine-tuning solved our under-labeling problem. Whether it makes sense for you depends on three questions.
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Retrieve One Row from a Table, Not the Whole Table: Row-Level Chunks for RAG
Enterprise Document Intelligence [Vol.1 #7sexies] - The unit of retrieval doesn’t have to be a page or a paragraph. When the corpus carries tables, each body row with its column headers is a chunk in its own right, and it’s often the one row the reader asked about
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The Types of Dimensions in a Star Schema, and How to Use Them
Dimensions are one of the two main object types in dimensional modelling. But what are the different types of dimensions? And how can you use them?
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Bayesian Guardrails for AI Decisions: Measuring Uncertainty Before Automating Decisions
AI systems should not automate a decision simply because they can provide a prediction. A decision system should consider how uncertain the prediction is and defer if a mistake would be costly.
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How Benders Decomposition Works, Part II: Feasibility Cuts
Learning about Farkas' lemma and how it can inform Benders decomposition to learn from infeasibility, applied to the capacitated facility location problem.
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