I will build mlops pipelines and deploy ml models with fastapi

A trained AI or ML model is useless if it stays trapped in a Jupyter Notebook or crashes under production traffic.

I turn your Python models, LLM pipelines, and PyTorch/TensorFlow scripts into fast, containerized, and auto-scaling production APIs on your cloudbacked by enterprise banking standards.

In my day job, I architect and maintain AI decision systems for a major banking client, where 99.9% uptime, strict data security, and sub-second inference latency are non-negotiable. I bring that exact engineering rigor to your deployment.

What I Can Deploy and Automate For You:

1. API Wrapping & Containerization

Wrap PyTorch, TensorFlow, Scikit-Learn, XGBoost, or Hugging Face models into high-performance FastAPI microservices packaged in lightweight, production-ready Docker containers.

2. Cloud & Serverless GPU Deployment

Deploy your containerized service to AWS (EC2, ECS, Lambda, SageMaker), GCP (Cloud Run, Vertex AI), Azure, or cost-effective serverless GPU clouds (RunPod, Modal).

3. Production MLOps & CI/CD

Automated deployment workflows via GitHub Actions, model versioning (MLflow), health monitoring, logging, and auto-scaling to keep cloud costs low when idle.