Artificial intelligence: development, training and integration · ENGLISH
The ai model optimisation service defines scope, stages and review criteria around the project goal.
01 · CONTEXT
The work defines inputs, expected outputs and checks before a task is automated.
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. Turn this guidance into an observable goal: Measure quality, latency and resource use before choosing an optimisation.
Measurements of quality, response time and resource use before each change.
Compare the same test workload before and after without missing the agreed quality threshold.
03 · METHOD
Models, integration and evaluation
04 · ANSWERS
Measure quality, latency and resource use before choosing an optimisation. Measurements of quality, response time and resource use before each change.
Prepare the task, representative examples, permitted data, privacy constraints and target environment. Avoid sharing sensitive data in an initial enquiry.
Compare the same test workload before and after without missing the agreed quality threshold.