Halide Yılmaz Data
8 posts
Data8 minData Provenance and Training-Data Licensing: Can You Prove Where Your Dataset Came From?
How do you record training-data provenance through a preprocessing pipeline? The sharp rise in robots.txt restrictions, the GPAI training-data summary template, where the IETF work stands, and what the 2025-2026 copyright rulings mean for engineering practice.
Data8 minData Contracts for ML: Stopping the Schema Change That Silently Breaks Your Pipeline
A silent upstream change degrades a model without tripping a single data alert. What a data contract is, which sections ODCS defines, how to enforce one in CI, and why the tooling comparison every ranking page repeats is now out of date on ownership and licensing.
Data8 minDatasheets for Datasets: The Missing Half of the Model Card
A model card documents the model, not the dataset. Which questions a datasheet answers, how it maps onto Annex IV technical documentation, and why this is the one document that cannot be produced retroactively.
Data3 minData-Centric AI: Improving the Data Instead of the Model
Data-centric AI holds the model fixed and improves the dataset instead. How label consistency, coverage gaps and error analysis pull more accuracy out of the same architecture, explained with practical examples.
Data3 minActive Learning: Spending a Labelling Budget on the Examples That Matter
Labelling everything is expensive. Active learning picks the examples a model is least sure about, buying more accuracy from the same budget. Uncertainty sampling, diversity, and the cold-start problem.
Data8 minFeature Engineering: Meaningful Signals from Raw Data and the Leakage Traps
An intuitive guide to feature engineering: deriving meaningful features from raw data, scaling, encoding, and the data-leakage traps that make models collapse in production.
Data7 minSynthetic Data Generation: Overcoming Scarcity, Protecting Privacy, and Avoiding Model Collapse
Synthetic data is generated data that imitates the patterns of reality. We explain, with intuitive examples, how it overcomes data scarcity, protects privacy, how LLMs generate it, and the risk of model collapse.
Data5 minData Quality and Labeling: A Model's Real Fuel
Garbage in, garbage out: data cleaning, labeling strategies, synthetic data, and honest evaluation sets that make up a model's real fuel.