ml-consistency

What problem does it solve?

ML experiments often suffer from drift between training and inference pipelines. This skill ensures preprocessing, feature extraction, and data handling are aligned, enabling reliable model deployment.

Core Features & Use Cases

• Compare preprocessing steps across train and inference codebases to ensure identical normalization, encoding, and data types.
• Validate that model inputs, feature extraction, and data flow are consistent between training and deployment.
• Use cases include validating experiment reproductions, auditing pipelines for deployment, and catching regressions early.

Quick Start

Run the ml-consistency checker on your experiment directory to verify alignment between train and inference components.