Independent Study, AI Platform Development
Responsibilities include developing and maintaining server-based AI platforms to support research projects, ensuring platform reliability and stability. Custom APIs are created to meet specific research needs, enabling efficient data handling and system integration while maintaining data integrity.
Proficiency in Python is applied to build research tools and applications, while Docker is used for containerization, ensuring scalable deployment. JavaScript and HTML/CSS support front-end development, and web frameworks like Flask, Django, and FastAPI are used to develop RESTful APIs for research purposes.
Expertise in server deployment on cloud platforms like AWS, GCP, and Azure ensures reliable and scalable systems. SQL and NoSQL databases are managed for secure, efficient data storage. Git version control supports collaboration and high-quality code management.
Expertise in server deployment on cloud platforms like AWS, GCP, and Azure ensures reliable and scalable systems. SQL and NoSQL databases are managed for secure, efficient data storage. Git version control supports collaboration and high-quality code management.
(Tools & Stack)
Python (Programming Language) · Deep Learning · Keras · Research · Data Science · Computer Science · Software Configuration Management · Natural Language Processing (NLP) · Human Subjects Research · Critical
(Process & Contribution)
Assistance is provided in deploying machine learning models as APIs, focusing on security, performance optimization, and scalability.
Authentication protocols are implemented, and monitoring tools track system health and usage.
Performance optimization is achieved through asynchronous processing to enhance the efficiency of research tools.
