ml-consistency

Compare train and inference pipelines for consistent preprocessing and feature extraction.

1|Updated Jan 3, 2022

Search Tags:#consistency #validation #preprocessing #pipelines #inference #ml #train

Ready to run

npx skills add https://github.com/daikichiba9511/dotfiles --skill ml-consistency

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ml-consistency
Source: https://github.com/daikichiba9511/dotfiles/tree/main/private_dot_codex/skills/ml-consistency
Command: npx skills add https://github.com/daikichiba9511/dotfiles --skill ml-consistency

SYSTEM DOCUMENTATION & REQUIREMENTS

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.

Frequently Asked Questions about ml-consistency

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I ensure preprocessing consistency between train and inference ML pipelines?▼

To ensure preprocessing consistency, you compare train and inference pipelines to verify identical normalization, encoding, and data types. This prevents feature extraction drift and guarantees reliable model deployment.

Why does my ML model performance drop during inference compared to training?▼

Model performance drops during inference when data handling drifts from training expectations. Validating that model inputs, feature extraction, and data flow match across training and deployment catches regressions early.

How do I validate ML experiment reproductions for deployment readiness?▼

You validate experiment reproductions by running a consistency check across your experiment directory to verify alignment between train and inference components. This confirms config alignment and shared modules are correctly applied.

Can I audit data pipelines for shared module consistency across ML experiments?▼

Yes, you can audit data pipelines by applying consistency checks across ML experiments. This covers common training and inference scripts, ensuring shared modules and config alignment produce structured reporting of findings.

What is training and inference pipeline drift in machine learning?▼

Training and inference pipeline drift occurs when preprocessing and feature extraction steps diverge between codebases. Identifying this drift ensures model inputs and data flow remain consistent from training to deployment.