In the development of our algorithm, we encountered a significant challenge in sourcing a dataset that was comprehensive enough to meet our specific requirements. After an extensive search, it became clear that no existing dataset adequately covered the breadth and depth of data we needed. This realization led us to the decision to develop our own dataset from scratch. The process involved meticulously defining our data requirements, identifying and collecting data from multiple sources, and then cleaning and organizing this data into a structured format. This effort was critical in ensuring that our algorithm had access to high-quality and relevant data, tailored specifically to its needs, thereby significantly enhancing its performance and accuracy.

On the technical implementation side, one of the most time-consuming aspects of our project was the hosting on AWS, particularly the creation of a Lambda function to handle requests. This process involved several steps, starting from the initial setup of the AWS environment, configuring the Lambda function, coding the function to process incoming requests efficiently, and integrating it with other AWS services for a seamless operation. Despite the complexity and the steep learning curve associated with AWS Lambda, its serverless computing model offered us the flexibility and scalability we needed. It allowed our application to handle requests without the need for a traditional server setup, thereby reducing operational costs and improving the reliability of our service. The journey through AWS Lambda's intricacies, from IAM roles and permissions to API Gateway integration, was challenging but ultimately rewarding, providing us with a robust and scalable solution that perfectly met our project's demands.