Answer
Expose models using Flask, FastAPI, or Django.
Containerize with Docker.
Use CI/CD and cloud platforms for production.
Containerize with Docker.
Use CI/CD and cloud platforms for production.
Quick Answer
Deploy Python ML models: Flask or FastAPI as REST API wrapper around the model. Serialize model with joblib/pickle, load on startup. Docker container for portability. Kubernetes for scaling. MLflow for experiment tracking and model registry. BentoML or Seldon for ML-specific serving with versioning. Monitor predictions in production (data drift, performance degradation). Batch prediction jobs for non-real-time use cases.
S
SugharaIQ Editorial Team
Verified Answer
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Source: SugharaIQ