Artificial intelligence: development, training and integration · ENGLISH
Service page for projects serving Sao Paulo. Organise data, objectives and evaluation to measure each training change. For Sao Paulo, separating audiences, features and priorities helps review products with multiple goals.
01 · CONTEXT
For Sao Paulo, separating audiences, features and priorities helps review products with multiple goals.
02 · PRACTICAL BRIEF
Models, integration and evaluation
Define the task, representative examples, permitted data, evaluation goal and runtime environment. Leave sensitive data out of the initial brief. Before finalising the brief for Sao Paulo, confirm with the people involved: For Sao Paulo, separating audiences, features and priorities helps review products with multiple goals.
Documented data preparation, training configuration and validation set.
Check separation between training and evaluation and record reproducible metrics.
03 · METHOD
Models, integration and evaluation
04 · ANSWERS
Organise data, objectives and evaluation to measure each training change. Documented data preparation, training configuration and validation set.
Prepare the task, representative examples, permitted data, privacy constraints and target environment. Avoid sharing sensitive data in an initial enquiry.
Check separation between training and evaluation and record reproducible metrics.