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mlops-industrialization

Convert notebook prototypes into a distributable Python package with a src layout, a domain/io/application split, and validated OmegaConf plus Pydantic configuration. Use when moving code out of notebooks or designing entrypoints.

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Precio sin confirmar★ 22 Estrellas de GitHubRegistro actualizado · 13 sept 2026agent-skill

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MLOps Industrialization

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 3.14
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.

Directory Tree:

my-project/
├── pyproject.toml       # Dependencies & Metadata
├── uv.lock              # Pinned Python dependencies
├── mise.toml            # Task vocabulary & pinned tools
├── mise.lock            # Pinned tool binaries
├── AGENTS.md            # Instructions for AI agents
├── 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)

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

2. Modularity & Paradigm (Hybrid Style)

Balance structure with predictability.

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).
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).
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.

Define Script: Create src/my_package/scripts.py with a main() function.

Register: Add to pyproject.toml:

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

CLI Execution:
- Dev: uv run my-tool (No install needed).
- Prod: pip install . -> my-tool (Installed on PATH).
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).

Define Schema (Pydantic):

Create a class that defines expected types and defaults.

from pydantic import BaseModel


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

Parse & Validate (OmegaConf):

Load YAML, merge with CLI args, and validate against the schema.

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))

Secrets: Use Environment Variables (os.getenv) or pydantic-settings, never commit them.

5. MLflow Services as I/O Objects

Tracking is a side effect, so it belongs in the I/O layer behind a small, configurable service.

Backend: Default the tracking and registry URIs to a SQL store — sqlite:///mlflow.db locally, a Postgres or HTTP tracking server in production. The file store is deprecated in MLflow 3.15 and does not support the model registry, so it is not a valid default for a package meant to reach production.
Configuration, not constants: Expose tracking_uri, registry_uri, experiment_name, and autolog as validated fields, so the same package runs against a laptop database and a shared server without a code change.
Lifecycle: Give the service explicit start()/stop() methods called by the application layer, never at import time.

6. Documentation & Quality

Make code usable and maintainable.

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

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.
    """

Type Hints: Use modern Python typing (list[str], X | Y) everywhere; ty (0.0.69+) checks them.
Instructions: Record the layer boundaries, the naming conventions, and the exact commands in AGENTS.md so assistants stop guessing where new code belongs.
Gate: mise run all (format -> check -> test -> build) must pass before a refactor is considered finished — see mlops-validation.

7. 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 and does ty check pass?
Entrypoints: Is the CLI registered in pyproject.toml?
Tracking: Does the MLflow service default to a SQL backend rather than the file store?
Gate: Does mise run all pass?

Ver fuente ↗

Metadatos del archivo

name: mlops-industrialization
description: Convert notebook prototypes into a distributable Python package with a src layout, a domain/io/application split, and validated OmegaConf plus Pydantic configuration. Use when moving code out of notebooks or designing entrypoints.
license: MIT
metadata:
  author: Médéric HURIER (Fmind)
  source: github.com/MLOps-Courses/mlops-coding-skills/tree/main/mlops-industrialization
  created: 2026-01-25
  updated: 2026-08-10

Ver texto original

---
name: mlops-industrialization
description: Convert notebook prototypes into a distributable Python package with a src layout, a domain/io/application split, and validated OmegaConf plus Pydantic configuration. Use when moving code out of notebooks or designing entrypoints.
license: MIT
metadata:
  author: Médéric HURIER (Fmind)
  source: github.com/MLOps-Courses/mlops-coding-skills/tree/main/mlops-industrialization
  created: 2026-01-25
  updated: 2026-08-10
---

# MLOps Industrialization

## 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 3.14
- **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              # Pinned Python dependencies
   ├── mise.toml            # Task vocabulary & pinned tools
   ├── mise.lock            # Pinned tool binaries
   ├── AGENTS.md            # Instructions for AI agents
   ├── 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)
   ```

1. **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).
1. **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).
1. **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.
1. **Register**: Add to `pyproject.toml`:

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

1. **CLI Execution**:
   - **Dev**: `uv run my-tool` (No install needed).
   - **Prod**: `pip install .` -> `my-tool` (Installed on PATH).
1. **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
   ```

1. **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))
   ```

1. **Secrets**: Use Environment Variables (`os.getenv`) or `pydantic-settings`, never commit them.

### 5. MLflow Services as I/O Objects

Tracking is a side effect, so it belongs in the I/O layer behind a small, configurable service.

1. **Backend**: Default the tracking and registry URIs to a SQL store — `sqlite:///mlflow.db` locally, a Postgres or HTTP tracking server in production. The file store is deprecated in MLflow 3.15 and does not support the model registry, so it is not a valid default for a package meant to reach production.
1. **Configuration, not constants**: Expose `tracking_uri`, `registry_uri`, `experiment_name`, and `autolog` as validated fields, so the same package runs against a laptop database and a shared server without a code change.
1. **Lifecycle**: Give the service explicit `start()`/`stop()` methods called by the application layer, never at import time.

### 6. 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.
       """
   ```

1. **Type Hints**: Use modern Python typing (`list[str]`, `X | Y`) everywhere; `ty` (0.0.69+) checks them.
1. **Instructions**: Record the layer boundaries, the naming conventions, and the exact commands in `AGENTS.md` so assistants stop guessing where new code belongs.
1. **Gate**: `mise run all` (format -> check -> test -> build) must pass before a refactor is considered finished — see [mlops-validation](../mlops-validation/SKILL.md).

### 7. 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 and does `ty check` pass?
- [ ] **Entrypoints**: Is the CLI registered in `pyproject.toml`?
- [ ] **Tracking**: Does the MLflow service default to a SQL backend rather than the file store?
- [ ] **Gate**: Does `mise run all` pass?

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Licencia: MIT
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Licencia: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
Falta aprobación de revisión por IA
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 22 GitHub stars
Stars/forks activity: 22 stars, 4 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

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Fuente y notas de uso

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Repositorio fuente: MLOps-Courses/mlops-coding-skills
Licencia: MIT
Versión: Unknown
Último push de GitHub: 10 ago 2026
Registro actualizado: 13 sept 2026
Ruta de instrucciones: mlops-industrialization/SKILL.md @ 4a146e6c4d47

Versión declarada en el registro; consulta las versiones de la fuente.

Calidad

49/100

Requiere revisión

Confianza

54/100

Do not auto-install

Auditoría

66/100

Requiere revisión

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Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
Falta aprobación de revisión por IA
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 22 GitHub stars
Stars/forks activity: 22 stars, 4 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

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    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Low GitHub adoption signal",
      "AI review approval is missing",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "GitHub adoption: 22 GitHub stars",
      "Stars/forks activity: 22 stars, 4 forks; issue activity unavailable in current metadata"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 49,
    "label": "Needs review"
  },
  "supply": {
    "track": "Design and creative production",
    "scenario": "Design and creative",
    "maintenance": "2mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "Low GitHub adoption signal",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "AI review approval is missing",
    "Quality score needs review"
  ],
  "agent_contract": {
    "task_input": "Use mlops-industrialization in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 62/100 Manual review",
      "Audit: 66/100 Needs review",
      "Safety: 22/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "mlops-courses-mlops-industrialization (mlops-industrialization)",
      "install_command": "npx skills add MLOps-Courses/mlops-coding-skills --skill mlops-industrialization",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "mlops-courses-mlops-industrialization",
      "task": "Use mlops-industrialization in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/mlops-courses-mlops-industrialization",
    "api": "https://www.openagentskill.com/api/agent/skills/mlops-courses-mlops-industrialization",
    "audit": "https://www.openagentskill.com/skills/mlops-courses-mlops-industrialization/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=mlops-courses-mlops-industrialization&task=Use%20mlops-industrialization%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20mlops-industrialization%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20mlops-industrialization%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/mlops-courses-mlops-industrialization/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/mlops-courses-mlops-industrialization"
  }
}

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Creador: Médéric HURIER (Fmind)
Fuente: MLOps-Courses/mlops-coding-skills
Indexado por: Índice comunitario de OpenAgentSkill

La atribución enlaza al repositorio público o al perfil del creador. Los creadores pueden reclamar la ficha para actualizar las señales de propiedad.

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