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Constraint

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

A system constraint is a limitation imposed by the capabilities, configuration, interfaces, or operating conditions of a customer system.

A system constraint is a limitation imposed by available data, business conditions, customer-system capabilities, configuration, interfaces, or operating conditions. Identify and document constraints during exploratory data analysis before committing to model development. section 6.4

Classify constraints before selecting the solution:

  • Data constraints limit what information is available or usable. section 6.4
  • Business constraints define acceptable opportunities and solution boundaries. section 6.4
  • Customer-system constraints restrict how the resulting system can operate. section 6.4
  • Ethical constraints can make a proposed system unacceptable and stop delivery when harm cannot be resolved. section 6.4

Once constraints and model requirements are understood, solve them through the design and development work of the modeling sprint. Treat unresolved constraints as delivery risks, not late implementation details. section 6.4

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

How deep it goes
Defines data, business, system, and ethical constraints, then directs teams to document and resolve them without detailing implementation methods.
Examples
Uses data limitations, business boundaries, customer-system restrictions, and unacceptable stakeholder harm as constraint examples.
What it assumes
Assumes exploratory data analysis has established usable data and surfaced relevant business, system, and ethical conditions.
What you can do after
Evaluate/understand constraints, document them, and use them to shape feasible model-development decisions.
How it connects
Connects exploratory data analysis, ethical assessment, business constraints, data constraints, model requirements, and model development.

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