Designing a modular ML training CLI

SECTION // WHY A REGISTRY

A dynamic model registry keeps model selection separate from command parsing and training orchestration. That makes the CLI easier to extend without turning every new model into a special case.

SECTION // A REPEATABLE WORKFLOW

MLCLI organizes training, evaluation, experiment tracking, and exporting around reusable trainer interfaces. Scikit-learn and TensorFlow families can share the workflow while keeping framework-specific details isolated.

SECTION // THE ENGINEERING LESSON

Developer tooling becomes valuable when the happy path is fast and the architecture leaves room for new experiments. Clear boundaries matter more than clever abstractions.