Artificial intelligence: development, training and integration · ENGLISH
Service page for projects serving Caracas. Decide whether fine-tuning fits the task and define evaluation examples before tuning. In Caracas, comparing offers is easier when scope, next steps and contact routes are clear.
01 · CONTEXT
In Caracas, comparing offers is easier when scope, next steps and contact routes are clear.
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 Caracas, confirm with the people involved: In Caracas, comparing offers is easier when scope, next steps and contact routes are clear.
An example schema, training format, configuration and base model version.
Evaluate held-out data and check for regressions on tasks the model already handled.
03 · METHOD
Models, integration and evaluation
04 · ANSWERS
Decide whether fine-tuning fits the task and define evaluation examples before tuning. An example schema, training format, configuration and base model version.
Prepare the task, representative examples, permitted data, privacy constraints and target environment. Avoid sharing sensitive data in an initial enquiry.
Evaluate held-out data and check for regressions on tasks the model already handled.