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I will build and deploy a production fastapi ml model api with docker
Umar Fayyaz
About this gig
Your ML model works great in a notebook. But "it works on my machine" isn't a deployment strategy, and duct-taped Flask scripts crash under real traffic.
I build production-grade, containerized APIs that turn your trained model (PyTorch, Scikit-Learn, XGBoost, TensorFlow) into a fast, reliable service your team or app can actually call.
What you get:
- Async FastAPI backend, non-blocking, built to handle concurrent requests without bottlenecks
- Docker + Compose setup portable, ready to deploy on any VPS or cloud provider in minutes
- Pydantic input validation bad requests get rejected before they touch your model
- Auto-generated Swagger docs your frontend/mobile team can test the API immediately; no extra documentation needed
- ONNX optimization (Premium) smaller memory footprint, faster inference
Your inference logic runs in its own isolated container, so your main app stays lightweight, fast, and cheap to host and you can scale the model service independently.
I've deployed real-time YOLO detection pipelines and GNN-based ML systems to production, so I know the difference between a demo and something that survives real traffic.
Have a specific model or framework in mind? Message me!
Get to know Umar Fayyaz
Umar Fayyaz
AI Engineer for Computer Vision and Generative AI
- FromPakistan
- Member sinceNov 2020
- Avg. response time1 hour
- Last delivery3 months
Languages
English
I am an expert AI Engineer & Python Developer architecting scalable, end-to-end AI solutions. I bridge the gap between Computer Vision and Generative AI.
Core Expertise:
• Computer Vision: Real-time object detection & multi-target tracking (YOLO, PyTorch, OpenCV).
• GenAI & LLMs: RAG pipelines, multi-agent systems & Local LLMs via LangChain.
• Backend: High-concurrency FastAPI & GPU acceleration.
I deliver production-ready AI applications. Let’s automate your next big idea!
My Portfolio
FAQ
What files or assets do I need to provide to get started?
You will need to provide your trained model weights (such as a .pkl, .h5, .pt, or .onnx file), a sample dataset or a clear explanation of the input/output JSON schema, and your target deployment hardware specifications.
Why do you use FastAPI instead of Flask for machine learning APIs?
FastAPI is asynchronous (async/await), which allows it to handle concurrent API requests without blocking your system threads. It also includes native Pydantic data validation and auto-generates Swagger documentation, making it significantly faster and safer for serving custom ML models.
How does Docker help with my machine learning deployment?
Machine learning libraries (like PyTorch and TensorFlow) are notoriously sensitive to specific Python versions and system dependencies. Docker packages your model, dependencies, and FastAPI code into an isolated container. This guarantees that if the API runs perfectly on my machine, it will run.
Can you optimize my model if it runs too slowly during inference?
Yes! For the Premium package, I will optimize your model weights by converting them to the ONNX (Open Neural Network Exchange) format or a TensorRT runtime engine. This process prunes overhead, slashes inference latency, and optimizes CPU/GPU memory utilization.
Will my frontend or mobile developer be able to connect to this API easily?
Absolutely. The FastAPI backend automatically creates a live interactive UI at /docs (Swagger UI). Your developers can see the exact input data types required, click a button to test the endpoints directly in their browser, and see the exact JSON response your model generates.
