What's included

• API Development using Python Flask

  Develop a Flask-based API that accepts and processes user requests. Design RESTful endpoints for submitting input data and retrieving results. Implement request validation to ensure proper data format.

• Machine Learning Model Integration

  Load and serve open-source models from Hugging Face using the Diffusers library. If locally model weights are available, configure the API to load and run them. Implement dynamic model selection (e.g., allow users to choose different models).

• Asynchronous Processing with Redis Queues

  Use Redis for job queuing when handling multiple requests. Ensure non-blocking API responses with job status tracking. Implement retry mechanisms for failed tasks.

• Dockerization & Deployment

  Create a Dockerfile for containerizing the API and its dependencies. Use Docker Compose to orchestrate multiple services (Flask, Redis, Worker). Ensure GPU acceleration support (if applicable, using NVIDIA Docker).

• API Documentation

  Provide Swagger (OpenAPI) documentation for easy integration. Include Postman collection for API testing. Provide usage examples for different ML models.

• Performance Optimization

  Optimize ML inference for faster response times (ONNX, TensorRT if applicable). Load balancing setup if needed for high-concurrency environments.

• Deployment & Handover

  Deploy API to AWS, Azure, or on-premise environments. Provide detailed documentation for usage, maintenance, and troubleshooting. Conduct a knowledge transfer session for the client’s team.