Data ml recommended
ML Model Deployment
Machine learning model deployment patterns covering FastAPI serving, MLflow integration, containerization, inference optimization, and production monitoring.
- Difficulty
- advanced
- Read time
- 1 min read
- Version
- v1.0.0
- Confidence
- established
- Last updated
Quick Reference
ML Deployment: FastAPI for lightweight serving, MLflow for model registry. Container models with dependencies. ONNX Runtime for cross-platform inference. Batch requests for GPU efficiency. Monitor latency, throughput, drift. Version models with registry. Use Triton/KServe for scale. Health checks and graceful shutdown.
Use When
- Deploying ML models to production
- Building inference APIs
- Model serving infrastructure
- MLOps pipelines
Skip When
- Research/experimentation only
- Batch processing only
- Edge deployment
ML Model Deployment
Machine learning model deployment patterns covering FastAPI serving, MLflow integration, containerization, inference optimization, and production monitoring.