Artificial intelligence: development, training and integration · ENGLISH
Service page for projects serving Sao Paulo. Choose a model and pipeline with attention to licence, resources and task. 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.
A model, tokenizer, version, licence and runtime selection.
Check licence and dependencies and run inference with representative inputs.
03 · METHOD
Models, integration and evaluation
04 · ANSWERS
Choose a model and pipeline with attention to licence, resources and task. A model, tokenizer, version, licence and runtime selection.
Prepare the task, representative examples, permitted data, privacy constraints and target environment. Avoid sharing sensitive data in an initial enquiry.
Check licence and dependencies and run inference with representative inputs.