Kddedt

Kddedt is a term used in data science and software engineering to describe a conceptual framework that seeks to integrate knowledge discovery, data engineering, and testing within a single lifecycle. It is not a fixed standard, but rather a label that appears in research papers and industry discussions to emphasize end-to-end rigor in data-driven projects.

The acronym is a portmanteau combining Knowledge Discovery in Databases (KDD) with elements of data engineering and testing. Its usage varies: some writers treat Kddedt as a blueprint for building reproducible data pipelines, while others use it more loosely to signal a holistic approach to constructing, validating, and maintaining data-centric systems.

A typical Kddedt workflow encompasses data ingestion and cleaning, feature extraction and management (often via a feature store), model development and experimentation, automated evaluation and testing, and deployment with monitoring. Proponents stress the importance of reproducibility, governance, and continuous testing across model updates and data changes.

Because there is no formal standard, the boundaries of Kddedt differ among sources, and it is sometimes discussed alongside related concepts such as MLOps, data engineering, and software testing rather than as a separate, codified methodology.

See also: Knowledge Discovery in Databases; Data Engineering; MLOps; Software Testing.