# MLOps Industrialization

> Guide to transform prototypes into robust, distributable Python packages using the src layout, hybrid paradigm, and strict configuration management.

- **Type:** Skill
- **Install:** `agentstack add skill-mlops-courses-mlops-coding-skills-mlops-industrialization`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [MLOps-Courses](https://agentstack.voostack.com/s/mlops-courses)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [MLOps-Courses](https://github.com/MLOps-Courses)
- **Source:** https://github.com/MLOps-Courses/mlops-coding-skills/tree/main/mlops-industrialization
- **Website:** https://mlops-coding-course.fmind.dev/0.%20Overview/0.6.%20Resources.html

## Install

```sh
agentstack add skill-mlops-courses-mlops-coding-skills-mlops-industrialization
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# 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 & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [MLOps-Courses](https://github.com/MLOps-Courses)
- **Source:** [MLOps-Courses/mlops-coding-skills](https://github.com/MLOps-Courses/mlops-coding-skills)
- **License:** MIT
- **Homepage:** https://mlops-coding-course.fmind.dev/0.%20Overview/0.6.%20Resources.html

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** yes
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-mlops-courses-mlops-coding-skills-mlops-industrialization
- Seller: https://agentstack.voostack.com/s/mlops-courses
- Browse the marketplace: https://agentstack.voostack.com/browse

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