ML EngineerMLOps and CI/CDBeginnerSingle prompt

Training Pipeline as Code AI Prompt

This prompt refactors an ad-hoc training script into a reproducible pipeline with configuration management, stage separation, artifact versioning, and a CLI. It is useful when a one-off training file has grown into something that needs repeatable execution and maintenance.

Prompt text
Refactor this ad-hoc training script into a reproducible, configurable ML pipeline.

1. Configuration management:
   - Move all hyperparameters and paths to a config file (YAML or JSON)
   - Use OmegaConf or Hydra for hierarchical config with command-line overrides
   - Never hardcode paths — all paths are config variables with sensible defaults
   - Log the full resolved config at the start of every run

2. Pipeline stages as separate functions or classes:
   - data_preprocessing(): validate, clean, and split data
   - train(): train model with given config
   - evaluate(): evaluate on test set and return metrics dict
   - export(): save model in deployment format
   - Each stage is independently runnable and testable

3. Artifact management:
   - Every run saves to a versioned output directory: outputs/{run_id}/
   - Artifacts: model checkpoint, config copy, metrics JSON, training plots
   - Symlink outputs/latest → most recent run for convenience

4. CLI interface:
   - python train.py --config configs/base.yaml --overrides learning_rate=1e-4
   - Subcommands: train, evaluate, export, full (all stages)

5. Dependency management:
   - requirements.txt with pinned versions
   - Optional: pyproject.toml with extras for training vs inference

6. Entry point guard:
   - All DataLoader workers require if __name__ == '__main__': guard on Windows

Return: refactored pipeline structure, Hydra config setup, and CLI interface.

When to use this prompt

Use case 01

when a training script has become too hardcoded or difficult to reproduce

Use case 02

when config files and command-line overrides are needed

Use case 03

when preprocessing, training, evaluation, and export should be separate stages

Use case 04

when each run should save versioned artifacts in a predictable structure

What the AI should return

A refactored ML pipeline structure with Hydra or OmegaConf config, CLI commands, stage functions, and artifact management conventions.

How to use this prompt

1

Open your data context

Load your dataset, notebook, or working environment so the AI can operate on the actual project context.

2

Copy the prompt text

Use the copy button above and paste the prompt into the AI assistant or prompt input area.

3

Review the output critically

Check whether the result matches your data, assumptions, and desired format before moving on.

4

Chain into the next prompt

Once you have the first result, continue deeper with related prompts in MLOps and CI/CD.

Frequently asked questions

What does the Training Pipeline as Code prompt do?+

It gives you a structured mlops and ci/cd starting point for ml engineer work and helps you move faster without starting from a blank page.

Who is this prompt for?+

It is designed for ml engineer workflows and marked as beginner, so it works well as a guided starting point for that level of experience.

What type of prompt is this?+

Training Pipeline as Code is a single prompt. You can copy it as-is, adapt it, or use it as one step inside a larger workflow.

Can I use this outside MLJAR Studio?+

Yes. The prompt text works in other AI tools too, but MLJAR Studio is the best fit when you want local execution, visible Python code, and reusable notebooks.

What should I open next?+

Natural next steps from here are Automated Retraining Trigger, CI/CD for ML Pipeline, Data Versioning with DVC.