ML Backend Framework Development

This project involved the development of a robust backend framework to support the deployment and management of machine learning models in production environments. The framework was designed to streamline the integration of ML models into applications, focusing on scalability, performance, and ease of use.

Key Features:

• Model Management: Developed a system for managing multiple machine learning models, including loading, versioning, and updating models seamlessly.
• API Integration: Built RESTful APIs using FastAPI to allow clients to interact with ML models, enabling real-time predictions and data processing.
• Scalability & Optimization: Implemented techniques for optimizing model inference and scaling the backend infrastructure to handle increased loads efficiently.
• Security & Authentication: Incorporated secure access methods, such as token-based authentication, to ensure that only authorized users could interact with the system.
• Monitoring & Logging: Developed monitoring tools to track model performance, log prediction requests, and handle errors effectively for troubleshooting and system optimization.
• Testing & Deployment: Conducted comprehensive unit testing and integrated the framework with Docker for easy deployment in cloud environments.

Technologies Used:

FastAPI, Docker, Python, Machine Learning (TensorFlow, PyTorch), Model Versioning, REST APIs, Security (JWT Tokens)

This backend framework significantly improved the process of deploying machine learning models, making them more accessible and efficient for real-time applications.