Why is a model usually packaged in a container for serving?
Options
- It makes the model more accurate
- It makes training faster
- It removes the need for monitoring
- The dependencies and versions are the same in development, testing and production, so it behaves the same everywhere (Correct option)
Answer
The dependencies and versions are the same in development, testing and production, so it behaves the same everywhere
Why
"It worked on my laptop" is common in ML because of the many libraries involved and their versions. A container image bundles the code, the model and the exact dependencies.
The same image is promoted from test to production and can be scaled by an orchestrator.
It does not change the accuracy: it makes the environment predictable.
Remember
Container = same environment everywhere.