I will deploy your machine learning model to production with mlops docker fastapi

Your model works in a Jupyter notebook but dies in production without Docker, APIs, and monitoring. I deploy ML models the way real teams ship: containerized, tested, documented, and ready for your stack.

WHAT YOU GET

Dockerized model with reproducible environment (Dockerfile + requirements)

FastAPI inference API with health checks and input validation

CI/CD-friendly structure (GitHub Actions or GitLab CI template)

Logging, monitoring hooks, and clear deployment README

Error handling for bad inputs, timeouts, and model load failures

Handover walkthrough so your team can redeploy without me

PERFECT FOR

Startups with a trained model that needs a real API

Data science teams without dedicated MLOps headcount

CV / NLP / tabular ML PyTorch, TensorFlow, scikit-learn, ONNX

Founders moving from Colab or SageMaker to VPS / AWS / GCP

WHY ME

Production ML engineer (MS Data Science) 2.5+ years deploying CV and OCR at Shufti Pro (YOLO, PaddleOCR, KYC systems). I ship serving code, not notebooks.

MESSAGE BEFORE ORDER: model format (.pt, .pkl, ONNX), expected QPS/latency, cloud target, and sample I/O.