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GDPR

As explained in Delivering Machine Learning Projects: From design to deployment

GDPR is the European Union regulation governing the processing and protection of personal data.

GDPR is a European Union regulation governing personal-data processing and protection. For ML projects, it limits proposed activities and makes regulatory awareness an explicit responsibility. section 2.6

Operationalize GDPR awareness through the pre-project responsibility and governance checklist:

  • Check and document applicable regulatory requirements, then challenge assumptions and record feedback. section 2.6
  • Create Privacy & Data Impact Assessment and Algorithmic Impact Assessment documents. section 2.6
  • Investigate domain-specific legislation alongside generic data and ML laws in each relevant jurisdiction. section 2.6

How Delivering Machine Learning Projects: From design to deployment covers it

How deep it goes
Defines GDPR as a limitation and turns compliance into documentation and investigation tasks, without detailing specific GDPR provisions.
Examples
Uses PS8 tickets, Privacy & Data Impact Assessments, Algorithmic Impact Assessments, and domain examples from medicine, finance, and industry.
What it assumes
Assumes a clear project hypothesis and teaches teams to identify relevant laws rather than treating legal ignorance as an excuse.
What you can do after
Evaluate proposed ML activities, document privacy impacts, and investigate applicable laws before delivery decisions.
How it connects
Connects GDPR with privacy, ethics, corporate responsibility, impact assessments, accountability, and domain-specific legislation.

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This explainer was generated by Manning's knowledge graph from the text of Delivering Machine Learning Projects: From design to deployment. The section links point to the parts of the book it draws on.