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Data annotation

As explained in Designing Deep Learning Systems: A guide for software engineers

The process of assigning annotations or target labels to data examples for supervised machine learning.

Data annotation assigns labels to data examples so they can become datasets for supervised machine learning. It belongs to data exploration, alongside data collection and data cleaning. section 1.1.1

Treat annotation as an early, flexible workflow rather than a formal production pipeline: teams may use scripts, manual copying, or web-based analysis tools while assembling relevant data. section 1.1.1

Organize annotation decisions around the development workflow:

  • Collect data from public and internal sources before labeling it. section 1.1.1
  • Choose whether labeling is performed in-house or outsourced. section 1.1.1
  • Clarify ownership across business stakeholders, data scientists, and data engineers before designing the system. section 1.1.2

How Designing Deep Learning Systems: A guide for software engineers covers it

How deep it goes
Places labeling in an informal early workflow and stops short of specifying annotation standards, quality controls, or a formal collection pipeline.
Examples
Uses public or internal data, outsourced or in-house labeling, Python or shell scripts, manual copying, and web-based analysis tools.
What it assumes
Assumes clarified business requirements before data scientists and data engineers begin collecting and labeling data.
What you can do after
Build it from scratch by collecting, labeling, and assembling datasets for algorithm evaluation and proof-of-concept models.
How it connects
Connects data collection, data cleaning, datasets, data exploration, model development, and cross-functional role ownership.
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This explainer was generated by Manning's knowledge graph from the text of Designing Deep Learning Systems: A guide for software engineers. The section links point to the parts of the book it draws on.