Description:

The annotation tool project was designed to facilitate the creation of high-quality datasets for training machine learning models. As a backend engineer, my primary role involved developing and maintaining a robust and scalable backend architecture to support various annotation tasks, audio data. The tool was built to streamline the process of tagging and organizing data, making it easier for machine learning models to learn from diverse, well-structured datasets.

My responsibilities included creating RESTful APIs to handle data flow between the frontend and backend, ensuring smooth user interactions, and managing large volumes of annotated data. I also focused on optimizing the tool for performance, ensuring that the annotation tasks could be performed efficiently, even with significant data loads. Additionally, I implemented security measures to protect sensitive data and maintained database structures that ensured easy retrieval and storage of annotations.

This annotation tool played a critical role in the development of training datasets, enabling faster and more accurate labeling processes, and ultimately contributing to the improvement of machine learning model performance.