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30 guides in Data for AI
The questions a dataset card should answer — what one row is, where the data came from, its licence and its quirks — before you use it.
Data for AI 1 min read 6 Apr 2026 For people
Accuracy, completeness, consistency, timeliness, validity and uniqueness: a framework for checking whether data is fit for purpose.
Data for AI 2 min read 5 Apr 2026 For people
Why data goes missing, how to find out, and the options — dropping, imputing, flagging — with their trade-offs.
Data for AI 2 min read 4 Apr 2026 For people
How to spot unusual values, decide whether they are errors or genuine extremes, and treat them appropriately.
Data for AI 2 min read 3 Apr 2026 For people
How to create high-quality labels: clear guidelines, measuring agreement, handling ambiguity and reducing cost.
Data for AI 2 min read 2 Apr 2026 For people
Delimiters, headers, quoting, encodings and types: how to read and write CSV files without the usual surprises.
Data for AI 2 min read 1 Apr 2026 For people
How the Parquet columnar format saves space and speeds up analysis, and when to choose it over CSV.
Data for AI 2 min read 31 Mar 2026 For people
Where to find reliable public datasets for learning and projects, and how to judge whether a dataset is suitable.
Data for AI 1 min read 30 Mar 2026 For people
CC0, CC BY, CC BY-SA, ODbL, non-commercial terms and government licences: what common data licences allow.
Data for AI 2 min read 29 Mar 2026 For people
A step-by-step checklist for getting to know a new dataset before modelling or reporting.
Data for AI 1 min read 28 Mar 2026 For people
How to clean data reproducibly: fixing types, standardising values, removing duplicates and documenting every step.
Data for AI 2 min read 27 Mar 2026 For people
Inner, left and outer joins, matching keys, and the classic mistakes — duplicated rows and silent data loss — to avoid.
Data for AI 2 min read 26 Mar 2026 For people
Why datasets need versions like code does, and practical approaches from snapshots to dedicated tools.
Data for AI 2 min read 25 Mar 2026 For people
Why training data must reflect the people and situations a model will face, and how to check and improve coverage.
Data for AI 2 min read 24 Mar 2026 For people
Creating varied training examples from existing ones — for images, text, audio and tabular data — and when it helps.
Data for AI 1 min read 23 Mar 2026 For people
What a feature store is, the problems it solves — reuse and training-serving consistency — and whether you need one.
Data for AI 2 min read 22 Mar 2026 For people
How synthetic data is generated, where it helps — testing, sharing, rare cases — and the limits on privacy and realism.
Data for AI 2 min read 21 Mar 2026 For people
How the world changes under a deployed model, how to detect drift, and what to do about it.
Data for AI 2 min read 20 Mar 2026 For people
Consent, transparency, minimisation and fair treatment of participants when collecting data for analysis and AI.
Data for AI 2 min read 19 Mar 2026 For people
A structured way to document how a dataset was created, what it contains and how it should be used.
Data for AI 2 min read 18 Mar 2026 For people