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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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Preis unbestätigt★ 22 GitHub-StarsVerzeichnis aktualisiert · 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?

Quelle ansehen ↗

Dateimetadaten

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

Originaltext anzeigen

---
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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Preis und Betriebskosten

Skill beziehen: Preis unbestätigt
Ausführen: Anforderungen unbestätigt. Agenten-, API- und Dienstkosten an der Quelle prüfen.
Lizenz: MIT
Preis unbestätigt: Der Preis ist noch nicht bestätigt. Vorhandene Quell- und Installationslinks bleiben verfügbar.

Kostenloser Bezug bedeutet nicht kostenlosen Betrieb. Preise sind keine Sicherheitsbewertung. Preisinformation einreichen →

Skill-Quelle erfasst

Ein Anleitungspfad ist erfasst. Das ist kein Ausführungstest und keine Sicherheits- oder Kompatibilitätsgarantie.

Vor Installation prüfen: Automatische Installation vermeiden

Lizenz: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
KI-Prüffreigabe fehlt
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

Vollständiges Audit öffnen

Tools sind Metadatenhinweise, keine getestete Kompatibilität. Prompts sind Vorschläge.

Mit einer kleinen Aufgabe beginnen

1Quelle lesen und Eingaben, Ergebnisse, Abhängigkeiten sowie Berechtigungen prüfen.
2Agent um einen Plan bitten. Einrichtung und Kosten vor einem isolierten Test genehmigen.
3Ergebnisse und geänderte Dateien prüfen. Nur tatsächliche Ausführungen melden und die Quellrevision aufbewahren.

Prüfe Abhängigkeiten, API-Schlüssel und externe Kosten in der Quelle. Öffentliche Repositories bedeuten nicht, dass alle Dienste kostenlos sind.

Quelle und Nutzungshinweise

ErfasstStatisch geprüft

Metadaten und Prüfungen dienen der Orientierung. Beliebtheit, Quellenerfassung und erfolgreiche Ausführung sind verschiedene Fakten.

Quell-Repository: MLOps-Courses/mlops-coding-skills
Lizenz: MIT
Version: Unknown
Letzter GitHub-Push: 10. Aug. 2026
Verzeichnis aktualisiert: 13. Sept. 2026
Anleitungspfad: mlops-industrialization/SKILL.md @ 4a146e6c4d47

Version aus den Verzeichnismetadaten; Releases der Quelle prüfen.

Qualität

49/100

Prüfung nötig

Vertrauen

54/100

Do not auto-install

Audit

66/100

Prüfung nötig

Vollständiges Audit öffnen Eval-Bericht ansehen

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
KI-Prüffreigabe fehlt
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

Verified installs: —
Ergebnisse: —

Kopieren ist keine Installation. Zahlen benötigen eine Erfolgsmeldung und garantieren keine allgemeine Qualität.

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Installations-API öffnen LLM-Textformat Manifest öffnen Auto-Auflösungsplan

Weitere Details

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      "uniqueAgents": 0,
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    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 66,
    "risk_level": "needs_review",
    "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"
  }
}

Für Ersteller

Quelle des Eintrags

Registry-indexiert

Beanspruchbar

Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.

Ersteller: Médéric HURIER (Fmind)
Quelle: MLOps-Courses/mlops-coding-skills
Indexiert von: OpenAgentSkill Community-Index

Die Zuordnung verlinkt auf das öffentliche Repository oder Creator-Profil. Creator können den Eintrag beanspruchen, um Eigentümersignale zu aktualisieren.

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Dieser Registry-indexiert-Eintrag wird Médéric HURIER (Fmind) zugeschrieben, ist aber noch nicht offiziell markiert. Beanspruche ihn, um ein verifiziertes Eigentümersignal hinzuzufügen und künftige Launch-, Installations- und Audit-Updates vertrauenswürdiger zu machen.

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