# Mlops Industrialization

> Guide to transform prototypes into robust, distributable Python packages using the src layout, hybrid paradigm, and strict configuration management. Use when this capability is needed.

- Skill: `tomevault-io/mlops-industrialization` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/mlops-industrialization`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/mlops-industrialization/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/mlops-industrialization

---


# MLOps Coding - Productionizing Skill

## Goal

To convert experimental code (notebooks/scripts) into a high-quality, distributable Python package. This skill enforces the **src/ layout**, a **Hybrid Paradigm** (OOP structure + Functional purity), and **Strict Configuration** to ensure scalability, security, and maintainability.

## Prerequisites

- **Language**: Python
- **Manager**: `uv`
- **Context**: Moving from `notebooks/` to `src/`.

## Instructions

### 1. Packaging Structure (`src` Layout)

Adopt the `src` layout to prevent import errors and separate source from tooling.

1. **Directory Tree**:

    ```text
    my-project/
    ├── pyproject.toml       # Dependencies & Metadata
    ├── uv.lock
    ├── README.md
    └── src/
        └── my_package/      # Main package directory
            ├── __init__.py
            ├── io/          # Side-effects (Datasets, APIs)
            ├── domain/      # Pure business logic (Models, Features)
            └── application/ # Orchestration (Training loops, Inference)
    ```

2. **Configuration**: Use `pyproject.toml` for all build metadata and dependencies.

### 2. Modularity & Paradigm (Hybrid Style)

Balance structure with predictability.

1. **Domain Layer (Pure)**:
    - **Rule**: Code here must be deterministic and free of side effects (no I/O).
    - **Use Case**: Feature transformations, Model architecture definitions.
    - **Style**: Functional (pure functions) or Immutable Objects (dataclasses).
2. **I/O Layer (Impure)**:
    - **Rule**: Isolate external interactions here.
    - **Use Case**: Loading data from S3, saving models to disk, logging to MLflow.
    - **Style**: OOP (Classes to manage connections/state).
3. **Application Layer (Orchestration)**:
    - **Rule**: Wire Domain and I/O together.
    - **Use Case**: Tuning, Training, Inference, Evaluation, etc.

### 3. Application Entrypoints

Create standard, installable CLI tools.

1. **Define Script**: Create `src/my_package/scripts.py` with a `main()` function.
2. **Register**: Add to `pyproject.toml`:

    ```toml
    [project.scripts]
    my-tool = "my_package.scripts:main"
    ```

3. **CLI Execution**:
    - **Dev**: `uv run my-tool` (No install needed).
    - **Prod**: `pip install .` -> `my-tool` (Installed on PATH).
4. **Guard**: Always use `if __name__ == "__main__":` in scripts to prevent execution on import.

### 4. Configuration Management

Decouple settings from code using **OmegaConf** (Parsing) and **Pydantic** (Validation).

1. **Define Schema (Pydantic)**:
    - Create a class that defines *expected* types and defaults.

    ```python
    from pydantic import BaseModel

    class TrainingConfig(BaseModel):
        batch_size: int = 32
        learning_rate: float = 0.001
        use_gpu: bool = False
    ```

2. **Parse & Validate (OmegaConf)**:
    - Load YAML, merge with CLI args, and validate against the schema.

    ```python
    import omegaconf

    # 1. Load YAML
    conf = omegaconf.OmegaConf.load("config.yaml")
    # 2. Merge with CLI (optional)
    cli_conf = omegaconf.OmegaConf.from_cli()
    merged = omegaconf.OmegaConf.merge(conf, cli_conf)
    # 3. Validate -> Returns a validated Pydantic object
    cfg: TrainingConfig = TrainingConfig(**omegaconf.OmegaConf.to_container(merged))
    ```

3. **Secrets**: Use Environment Variables (`os.getenv`), never commit them.

### 5. Documentation & Quality

Make code usable and maintainable.

1. **Docstrings**: Use **Google Style** docstrings for all modules, classes, and functions.

    ```python
    def calculate_metric(y_true: np.ndarray, y_pred: np.ndarray) -> float:
        """Calculates the accuracy score.

        Args:
            y_true: Ground truth labels.
            y_pred: Predicted labels.

        Returns:
            The accuracy as a float between 0 and 1.
        """
    ```

2. **Type Hints**: Use standard python typing (`typing`, `list[str]`) everywhere.

### 6. Best Practices Summary

- **Config != Code**: Never hardcode paths or hyperparams; use the `Pydantic + OmegaConf` pattern.
- **Entrypoints are APIs**: Design your CLI (`[project.scripts]`) as the public interface for your automation tools.
- **Immutable Core**: Keep your domain logic side-effect free; push I/O to the edges.

## Self-Correction Checklist

- [ ] **No Side Effects on Import**: Does `import my_package` run any code? (It shouldn't).
- [ ] **Src Layout**: Is code inside `src/`?
- [ ] **Config Safety**: Are secrets excluded from `pyproject.toml` and YAML?
- [ ] **Typing**: Are function signatures fully type-hinted?
- [ ] **Entrypoints**: Is the CLI registered in `pyproject.toml`?

---
> Source: [fmind/mlops-python-package](https://github.com/fmind/mlops-python-package) — distributed by [TomeVault](https://tomevault.io).
<!-- tomevault:4.0:skill_md:2026-06-25 -->