In the previous article, I extended a small Python data quality ETL starter with AI-ready data...
In the previous article, I extended a small Python data quality ETL starter with AI-ready data preparation.
The important constraint was that the workflow did not call an LLM API, generate embeddings, or train a model. It prepared structured data assets such as schema profiles, data dictionaries, validation summaries, feature-ready CSV files, and manifest files.
Previous article:
Preparing AI-Ready Data Without Calling an LLM API
This follow-up focuses on the v0.7.0 update of the same project:
In the first article, I walked through a small Python data quality ETL starter that reads messy CSV,...
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