What is one major task during the implementation phase of an AI project?

During the implementation phase of an AI project, validating AI system performance is a crucial task. This step ensures that the system functions as intended and meets the predefined requirements and performance metrics established during the earlier phases of the project. It involves evaluating how well the AI model performs in real-world scenarios, verifying accuracy, reliability, and efficiency across various conditions. This validation process helps identify any shortcomings and potential areas for improvement before full deployment, ensuring that the system provides value to users and meets their needs.

While collecting user feedback post-deployment is essential for ongoing improvement, it occurs after implementation. Developing model training guidelines is more relevant during the planning and development phases, as it is concerned with how to train the model effectively. Conducting market research typically happens before the project implementation, focusing on understanding market needs to guide the development process. Hence, the validation of AI system performance is a significant task that directly impacts the success of the AI project during its implementation.