This project aims to enhance the predictive accuracy of an existing watch classification model deployed in AWS SageMaker.

The current model, while functional, requires optimization to improve its ability to accurately identify watch brands from images. By utilizing an open-source dataset of watch images, learners will apply advanced machine learning techniques to fine-tune the model.

The goal is to develop a refined model prototype that demonstrates a significant and quantifiable improvement in brand identification accuracy.

The project will be conducted entirely within the AWS SageMaker environment, providing learners with hands-on experience in a real-world cloud-based machine learning platform. This project offers an excellent opportunity for learners to apply their classroom knowledge of machine learning and cloud computing to a practical problem.

The primary deliverable is a refined watch classification model prototype with improved accuracy in identifying watch brands. Additional deliverables include a comprehensive report detailing the techniques used for model optimization and a presentation summarizing the project's findings and results. All work will be documented and shared within the AWS SageMaker environment, ensuring transparency and reproducibility.