Generate deployment artifacts (API, Docker, monitoring)
# Deploy ML Model You are preparing an ML model for production deployment. Generate all necessary deployment artifacts following MLOps best practices. ## Your Task 1. **Generate API**: FastAPI endpoint for model serving 2. **Containerize**: Dockerfile for model deployment 3. **Setup Monitoring**: Prometheus/Grafana configuration 4. **Create A/B Test**: Traffic splitting infrastructure 5. **Document Deployment**: Deployment runbook ## Deployment Steps ### Step 1: Generate FastAPI App Creates: `api/main.py`, `api/models.py`, `api/predict.py` ### Step 2: Create Dockerfile Creates: ...
Automated deployment with pre-flight checks, staging validation, and production rollout
Deploy the application to the specified environment with comprehensive validation.
Safe, staged deployment with quality gates and rollback capability
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