BSc IT Project Guide: Cloud-Based Machine Learning Model Deployment
1. Objective
The primary objective of this project is to develop a cloud-based platform that facilitates the deployment of machine learning models using services like AWS SageMaker, Google AI Platform, or Azure ML. The system will allow users to upload trained models, serve predictions through APIs, and monitor model performance in real-time.
2. Scope
This project includes model packaging, containerization, deployment on cloud services, API integration, and a user interface for managing and monitoring deployed models.
3. Software & Hardware Requirements
• Python, Flask or FastAPI
• Docker
• AWS/GCP/Azure account
• HTML, CSS, JavaScript for UI
• Git and GitHub
• Basic laptop or desktop with internet access
4. System Design
• User Interface for uploading models and accessing APIs
• Backend for processing and API routing
• Integration with cloud services for deployment
• Monitoring and logging components
5. Modules
1. User Authentication
2. Model Upload and Versioning
3. Deployment Manager
4. API Gateway
5. Model Monitoring Dashboard
6. Use Cases
• A data scientist uploads a trained model and deploys it as
an API.
• A developer integrates the model API into a web application.
• An admin monitors traffic and performance metrics.
7. Conclusion
The cloud-based machine learning deployment system simplifies the process of making models production-ready. It offers scalability, ease of access, and maintainability using modern cloud technologies.