Phase 1: Model Development and Validation
Start with a well-validated model. Use proper train-test splits, cross-validation, and A/B testing to ensure your model performs well on unseen data.
Phase 2: Model Packaging
Use tools like Flask, FastAPI, or BentoML to wrap your model in an API. Containerize it with Docker to ensure consistency across environments.
Phase 3: CI/CD Pipeline
Set up continuous integration and deployment using GitHub Actions, GitLab CI, or Kubeflow. Automate model testing and deployment whenever new code or data is pushed.
Phase 4: Monitoring and Observability
Monitor model performance, data drift, and concept drift. Tools like Prometheus, Grafana, and Evidently AI can help you track these metrics.
Phase 5: Scaling and Optimization
As usage grows, optimize your model inference time and scale your infrastructure horizontally. Consider using model quantization or pruning for faster inference.