Artificial Intelligence

Algorithm Selection: The choice of algorithms that will be employed in the AI model, such as neural networks, decision trees, or support vector machines.

Data Requirements: Specifications for the type, quality, and quantity of data needed for training and evaluating the AI model.

Performance Metrics: Criteria used to evaluate the effectiveness of the AI model, such as accuracy, precision, recall, and F1 score.

Scalability: Specifications regarding how well the AI system can handle increasing amounts of data or user requests.

Hardware Requirements: The necessary computational resources, including processors, memory, and storage, required to run the AI system efficiently.

Integration Requirements: Guidelines for how the AI system will integrate with existing software, hardware, and workflows.

User Interface Design: Specifications for how users will interact with the AI system, including usability and accessibility considerations.

Ethical Guidelines: Standards that ensure the AI system adheres to ethical practices, including fairness, transparency, and accountability.

Security Measures: Specifications regarding the security protocols that must be in place to protect data and ensure the integrity of the AI system.

Deployment Environment: The context in which the AI system will operate, including cloud-based or on-premises environments.