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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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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?

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Métadonnées du fichier

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

Voir le texte 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?

Examiner la source

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Licence: MIT
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Réviser avant installation: Éviter l’installation automatique

Licence: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
L’approbation de revue IA est absente
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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Commencer par une petite tâche

1Lisez la source et confirmez entrées, résultats, dépendances et permissions.
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Vérifiez les dépendances, clés API et frais externes dans la source. Un dépôt public ne rend pas tous les services gratuits.

Source et conseils d’utilisation

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Métadonnées et examens sont indicatifs. Popularité, découverte et exécution réussie sont des faits distincts.

Dépôt source: MLOps-Courses/mlops-coding-skills
Licence: MIT
Version: Unknown
Dernier push GitHub: 10 août 2026
Registre mis à jour: 13 sept. 2026
Chemin des instructions: mlops-industrialization/SKILL.md @ 4a146e6c4d47

Version déclarée dans le registre ; vérifiez les versions de la source.

Qualité

49/100

Revue nécessaire

Confiance

54/100

Do not auto-install

Audit

66/100

Revue nécessaire

Ouvrir l’audit complet Voir le rapport d’évaluation

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
L’approbation de revue IA est absente
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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Résultats: —

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    "score": 66,
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    "risk_label": "Needs review",
    "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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Cette fiche a été indexée à partir de sources publiques et n’est pas marquée officielle tant qu’une revendication de mainteneur n’est pas approuvée.

Créateur: Médéric HURIER (Fmind)
Source: MLOps-Courses/mlops-coding-skills
Indexé par: Index communautaire OpenAgentSkill

L’attribution renvoie au dépôt public ou au profil du créateur. Les créateurs peuvent revendiquer la fiche pour mettre à jour les signaux de propriété.

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