All the work done to clean and organise raw data before it is used to train an AI — removing errors, standardising formats, and making sure it is in a usable shape.
All the work done to clean and organise raw data before it is used to train an AI — removing errors, standardising formats, and making sure it is in a usable shape.
Data preparation is the most time-intensive phase of AI development, often accounting for 60–80% of project effort. It encompasses: data cleaning (removing duplicates, fixing errors, handling missing values), transformation (normalisation, encoding, aggregation), integration (combining data from multiple sources), reduction (sampling, dimensionality reduction), and augmentation (generating synthetic samples to address gaps). EU AI Act Annex IV requires documentation of data preparation steps for high-risk systems, including justification of choices that affect the training set composition. ISO/IEC 5259 Part 4 addresses data preparation within a process framework for AI data quality.
Investing in reusable data preparation pipelines with version control and automated quality checks reduces the cost of iterative model development and ensures auditability of training data provenance.
Like a chef preparing ingredients before cooking — washing vegetables, measuring portions, chopping and marinating — the quality of the final dish depends as much on preparation as on cooking technique.
All the work done to clean and organise raw data before it is used to train an AI — removing errors, standardising formats, and making sure it is in a usable shape.
Data preparation is the most time-intensive phase of AI development, often accounting for 60–80% of project effort. It encompasses: data cleaning (removing duplicates, fixing errors, handling missing values), transformation (normalisation, encoding, aggregation), integration (combining data from multiple sources), reduction (sampling, dimensionality reduction), and augmentation (generating synthetic samples to address gaps). EU AI Act Annex IV requires documentation of data preparation steps for high-risk systems, including justification of choices that affect the training set composition. ISO/IEC 5259 Part 4 addresses data preparation within a process framework for AI data quality.
Investing in reusable data preparation pipelines with version control and automated quality checks reduces the cost of iterative model development and ensures auditability of training data provenance.