In our project, we encountered significant computational demands due to the extensive data processing required for training AI models. Initially, our existing computational resources were insufficient for some of the more demanding models, necessitating requests for additional resources. This process was time-con ing and highlighted the importance of incorporating resource availability into the project's planning phase. Future projects should conduct a thorough assessment of computational requirements and align them with available resources from the outset to minimize delays.

Our approach initially involved leveraging an existing API, which proved effective in the early stages. However, as the project evolved, we identified gaps in the API's functionality that were critical to our objectives. This realization led to the development of a custom API, a process that required additional time and resources. For future initiatives, a detailed analysis of preexisting APIs' capabilities relative to project requirements is advisable. Should gaps be identified, consider the feasibility of custom API development early in the project timeline.

The project's execution involved using specific tools and accessing particular datasets, which necessitated administrative setup and permissions. The setup process was more time-consuming than anticipated, primarily due to delays in administrative responses. To mitigate such issues in future projects, it is recommended to engage in proactive communication with all stakeholders to clearly identify necessary tools, datasets, and permissions at the project's inception. Early engagement and clear communication with the administrative teams can streamline the setup process and reduce project delays.