Containerizing Machine Learning Models with Docker
Why Containerization Matters in MLOps
Containerization packages your serialized model along with its dependencies and runtime environment. This guarantees that the model behaves consistently across development, staging, and production.
Benefits of Containerized ML
- Environment consistency
- Easy deployment
- Scalable orchestration
- Isolation of dependencies
Containers eliminate the classic "it works on my machine" problem in ML deployment.
