ML Model Deployment to Cloud
Budget: ₹1,500 – ₹12,500 INR
I am seeking an experienced developer/ML engineer to help deploy a pre-trained machine learning model to a cloud environment. The goal is to create a scalable, efficient, and reliable system that can serve predictions via an API or web interface.
Requirements:
Deploy a pre-trained ML model (PyTorch, TensorFlow, or Hugging Face) in a cloud environment (AWS, GCP, Azure, Runpod, or similar).
Ensure GPU acceleration is utilized if required for inference.
Develop a RESTful API using FastAPI, Flask, or similar frameworks to handle requests and return predictions.
Optionally, integrate a simple frontend/web interface to send inputs (images/text/data) and display model outputs.
Containerize the application using Docker for easy deployment and portability.
Provide documentation and setup instructions for running the service independently.
Ensure robust error handling, logging, and security measures for API endpoints.
Inputs & Outputs:
Input: Images, text, or other relevant data (depending on the model type).
Output: Model predictions in a structured format (JSON, video, image, or other).
Deliverables:
Fully functional cloud-hosted ML API/web application.
Docker container and deployment scripts.
Documentation covering setup, usage, and troubleshooting.
Skills Required:
Python, FastAPI/Flask
Docker and cloud deployment
GPU-based ML inference (PyTorch, TensorFlow, Hugging Face)
Optional: Frontend development (React, HTML/CSS)
Requirements:
Deploy a pre-trained ML model (PyTorch, TensorFlow, or Hugging Face) in a cloud environment (AWS, GCP, Azure, Runpod, or similar).
Ensure GPU acceleration is utilized if required for inference.
Develop a RESTful API using FastAPI, Flask, or similar frameworks to handle requests and return predictions.
Optionally, integrate a simple frontend/web interface to send inputs (images/text/data) and display model outputs.
Containerize the application using Docker for easy deployment and portability.
Provide documentation and setup instructions for running the service independently.
Ensure robust error handling, logging, and security measures for API endpoints.
Inputs & Outputs:
Input: Images, text, or other relevant data (depending on the model type).
Output: Model predictions in a structured format (JSON, video, image, or other).
Deliverables:
Fully functional cloud-hosted ML API/web application.
Docker container and deployment scripts.
Documentation covering setup, usage, and troubleshooting.
Skills Required:
Python, FastAPI/Flask
Docker and cloud deployment
GPU-based ML inference (PyTorch, TensorFlow, Hugging Face)
Optional: Frontend development (React, HTML/CSS)