This thesis focuses on the implementation and productionization of a deep learning framework for parcel delivery optimization. The student will work on developing, testing, and deploying a comprehensive machine learning solution that meets real-world requirements. Key aspects of the project include:

Deep Learning Framework Refinement and Productionization: Enhance and refine state-of-the-art deep learning models tailored to the specific requirements of parcel delivery optimization. This includes exploring various architectures, optimizing model performance, ensuring scalability, and avoiding overfitting.

Production-Ready Development: Transform the research prototype into a production-ready system. This involves code optimization, creating robust APIs, implementing proper error handling, and ensuring the solution meets industry standards for deployment.

System Integration and Testing: Work on integrating the deep learning framework with existing systems and conducting comprehensive testing to ensure reliability and performance under various conditions.

Performance Optimization: Focus on improving model efficiency, reducing inference time, and optimizing resource utilization to meet production requirements.

This thesis offers an excellent opportunity to gain hands-on experience in developing and deploying deep learning solutions in a real-world business context. You will work closely with both academic supervisors and industry partners, gaining valuable insights into the practical challenges of bringing machine learning models from research to production.