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Machine Learning Academy · レッスン

モデルのバージョン管理：ファイル名とメタデータが重要な理由

学習日、データセットのバージョン、指標スコアを含める命名規則を設計し、ガバナンスのためのJSONメタデータサイドカーを作成します。

「モデルのバージョン管理：ファイル名とメタデータが重要な理由」はCoddyKit上の無料Machine Learning Academyレッスンです。 これはレッスン2/4です。 下記で完全なレッスンを無料で読むことができます。その後、ブラウザ内の組み込みコードエディタと24時間対応のAIチューターでハンズオン演習できます。 これはMachine Learning Academy学習パスの一部であり、ウェブとCoddyKitアプリ全体で進捗が同期されます。 Machine Learning Academyコースには全4レッスンが含まれています。

このレッスンの一部はまだ翻訳されておらず、英語で表示されています。

The Problem Without Versioning

Without a disciplined versioning strategy, teams quickly accumulate model.pkl, model_v2.pkl, model_final.pkl, model_FINAL_v2.pkl files with no record of which was trained on what data, which metric it achieved, or which is actually running in production. This chaos leads to deploying stale models, losing the best checkpoint, or being unable to reproduce a past result for debugging.

What to Encode in the Filename

A good model filename should encode enough context to be self-describing: dataset, algorithm, date, and optionally the primary metric and the dataset version or git SHA. This makes the model registry folder a readable audit trail at a glance, without needing to open each file.

from datetime import date

def model_filename(dataset, algorithm, metric_name, metric_value,
                   data_version='v1', ext='joblib'):
    today = date.today().strftime('%Y%m%d')
    metric_str = f'{metric_name}{int(metric_value * 100)}'
    return f'{dataset}__{algorithm}__{today}__dv{data_version}__{metric_str}.{ext}'

# Examples
print(model_filename('titanic', 'rf', 'acc', 0.834, data_version='2'))
print(model_filename('fraud', 'xgb', 'auc', 0.971))
print(model_filename('cancer', 'logreg', 'f1', 0.955))

The JSON Metadata Sidecar

Every model file should have a companion JSON sidecar file with the same stem name. The sidecar documents everything the filename cannot: library versions, hyperparameters, training set size, test set performance across all metrics, feature list, and any notes about the training run. This is the minimum viable model card.

import json
import sklearn, sys
from datetime import datetime

def save_metadata(path, dataset, algorithm, params, metrics, features, notes=''):
    meta = {
        'model_path': path,
        'dataset': dataset,
        'algorithm': algorithm,
        'hyperparameters': params,
        'metrics': metrics,
        'features': features,
        'sklearn_version': sklearn.__version__,
        'python_version': sys.version.split()[0],
        'trained_at': datetime.utcnow().isoformat(),
        'notes': notes
    }
    meta_path = path.replace('.joblib', '_metadata.json')
    with open(meta_path, 'w') as f:
        json.dump(meta, f, indent=2)
    print('Metadata saved to:', meta_path)
    return meta

# Usage example
save_metadata(
    '/tmp/cancer__logreg__20260620__dv1__acc97.joblib',
    dataset='breast_cancer', algorithm='LogisticRegression',
    params={'C': 1.0, 'max_iter': 300},
    metrics={'accuracy': 0.9789, 'roc_auc': 0.9941, 'f1': 0.9831},
    features=['mean radius', 'mean texture', '... 30 total'],
    notes='Trained on full UCI breast cancer dataset'
)

Dataset Version Tracking

The training dataset itself must be versioned. A model trained on data_v1.csv and another on data_v2.csv should never have the same model identifier. Options for dataset versioning: store a Git SHA of the data file, record an MD5/SHA256 hash of the CSV, or use a data versioning tool like DVC (Data Version Control) which manages dataset lineage the same way Git manages code.

import hashlib

def file_hash(path, algo='sha256'):
    h = hashlib.new(algo)
    with open(path, 'rb') as f:
        for chunk in iter(lambda: f.read(65536), b''):
            h.update(chunk)
    return h.hexdigest()[:12]  # first 12 hex chars as short ID

# Example: hash the model file itself as a unique ID
model_path = '/tmp/cancer_model.joblib'
model_hash = file_hash(model_path)
print('Model hash (short):', model_hash)

Semantic Versioning for Models

Borrow from software engineering: use semantic versioning (MAJOR.MINOR.PATCH) for models. MAJOR: breaking change (different feature set or incompatible schema). MINOR: performance improvement with same API. PATCH: bug fix or minor recalibration. This convention helps downstream consumers understand the impact of updating their dependency on the model.

model_registry = [
    {'version': '1.0.0', 'algorithm': 'LogisticRegression', 'auc': 0.921,
     'note': 'Initial production model'},
    {'version': '1.1.0', 'algorithm': 'LogisticRegression', 'auc': 0.935,
     'note': 'Retrained on 3 months more data'},
    {'version': '2.0.0', 'algorithm': 'XGBoost', 'auc': 0.971,
     'note': 'New algorithm; feature set changed — incompatible schema'}
]

print('Model Registry:')
for entry in model_registry:
    print(f"  v{entry['version']}  AUC={entry['auc']}  {entry['note']}")

A Simple Local Model Registry

A lightweight model registry can be a directory with a registry.json file that indexes all saved models. Each entry records the filename, version, key metrics, and the production flag. The deployment script reads this file to determine which model to load.

import json
import os

REGISTRY_PATH = '/tmp/model_registry.json'

def register_model(filename, version, metrics, is_production=False):
    try:
        with open(REGISTRY_PATH) as f:
            registry = json.load(f)
    except FileNotFoundError:
        registry = []

    # Mark all as not-production if this one is production
    if is_production:
        for entry in registry:
            entry['is_production'] = False

    registry.append({
        'filename': filename,
        'version': version,
        'metrics': metrics,
        'is_production': is_production
    })

    with open(REGISTRY_PATH, 'w') as f:
        json.dump(registry, f, indent=2)
    print(f'Registered v{version} (production={is_production})')

register_model('cancer__logreg__v1.0.0.joblib', '1.0.0',
               {'accuracy': 0.979, 'auc': 0.994}, is_production=True)

Reading the Production Model from Registry

At inference time, the service loads the registry and finds the model flagged as production, then loads that specific file. This decouples the deployment script from hardcoded filenames — updating the production model only requires updating the registry flag, not modifying serving code.

import json
import joblib

def load_production_model(registry_path, model_dir='/tmp'):
    with open(registry_path) as f:
        registry = json.load(f)
    prod = next((r for r in registry if r['is_production']), None)
    if prod is None:
        raise RuntimeError('No production model registered!')
    path = f'{model_dir}/{prod["filename"]}'
    print(f'Loading production model: {prod["filename"]} (v{prod["version"]})')
    print(f'Metrics: {prod["metrics"]}')
    # return joblib.load(path)  # would load for real
    return None  # demo

load_production_model('/tmp/model_registry.json')

MLflow: Professional Model Registry

MLflow is the industry-standard tool for experiment tracking and model registry. It logs parameters, metrics, and artefacts for each training run; lets you compare runs via a UI; and provides a Model Registry with staging/production/archived states. For teams, MLflow replaces manual JSON registries with a robust, queryable backend.

import mlflow
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris
from sklearn.model_selection import cross_val_score
import numpy as np

X, y = load_iris(return_X_y=True)

with mlflow.start_run(run_name='logreg_iris_v1'):
    model = LogisticRegression(C=1.0, max_iter=200)
    cv_score = cross_val_score(model, X, y, cv=5).mean()

    mlflow.log_param('C', 1.0)
    mlflow.log_param('max_iter', 200)
    mlflow.log_metric('cv_accuracy', cv_score)

    model.fit(X, y)
    mlflow.sklearn.log_model(model, 'model')

    print(f'CV Accuracy: {cv_score:.4f}')
    print('Run logged to MLflow')

Tagging Model Versions

Tags add freeform key-value annotations to a model version — useful for recording experiment context, the analyst's name, the task type, or whether the model passed a fairness audit. Tags are queryable, making it easy to filter the registry by any attribute.

# Simulated registry entry with tags (no MLflow required)
model_entry = {
    'version': '2.1.0',
    'filename': 'fraud__xgb__2.1.0.joblib',
    'tags': {
        'analyst': 'data-science-team',
        'task': 'binary-classification',
        'fairness_audit': 'passed',
        'retrain_trigger': 'monthly-schedule',
        'deployment_region': 'eu-west-1'
    },
    'metrics': {'roc_auc': 0.971, 'precision': 0.83, 'recall': 0.79}
}

print('Model entry:')
print(json.dumps(model_entry, indent=2))

Automated Promotion Criteria

Define clear promotion criteria before any model goes to production: the new model must achieve at least X% AUC improvement, must pass a fairness check, must not degrade on any monitored demographic slice, and must complete inference within Y milliseconds. Encoding these criteria in code (not documentation) lets a CI/CD pipeline automate promotion decisions objectively.

def should_promote(new_metrics, baseline_metrics, min_auc_improvement=0.005):
    if new_metrics['auc'] < baseline_metrics['auc'] + min_auc_improvement:
        return False, 'AUC improvement too small'
    if new_metrics.get('fairness_delta', 0) > 0.05:
        return False, 'Fairness constraint violated'
    if new_metrics.get('latency_ms', 0) > 100:
        return False, 'Latency too high'
    return True, 'All criteria met'

baseline = {'auc': 0.921}
candidate = {'auc': 0.937, 'fairness_delta': 0.02, 'latency_ms': 45}

promote, reason = should_promote(candidate, baseline)
print(f'Promote: {promote} — {reason}')

Model Cards and Documentation

Beyond technical metadata, a model card (coined by Google) documents the model's intended use, limitations, training data characteristics, performance across demographic subgroups, and ethical considerations. For models making consequential decisions (loan approvals, medical triage), model cards are becoming a regulatory requirement. Include at minimum: intended use, out-of-scope uses, performance metrics, and known failure modes.

Quick Check

Test your understanding of model versioning and metadata from this lesson.

Lesson Recap

In this lesson you learned: descriptive filenames embedding dataset, algorithm, date, and metric make your model directory self-documenting, JSON metadata sidecars record library versions, hyperparameters, and metrics needed for governance and reproducibility, and a model registry (local JSON or MLflow) decouples serving code from hardcoded filenames. Next up we wrap a saved model in a FastAPI endpoint to serve predictions over HTTP.

よくある質問

「モデルのバージョン管理：ファイル名とメタデータが重要な理由」レッスンは無料ですか？

はい。「モデルのバージョン管理：ファイル名とメタデータが重要な理由」の完全なテキストはこのウェブで無料で読めます。インタラクティブに演習し（組み込みコードエディタと24時間対応のAIチューター）、Machine Learning Academyコースの残りをアンロックするには、CoddyKit PROにアップグレードしてください。 Machine Learning Academyコースには全4レッスンが含まれています。

「モデルのバージョン管理：ファイル名とメタデータが重要な理由」で何を学びますか？

学習日、データセットのバージョン、指標スコアを含める命名規則を設計し、ガバナンスのためのJSONメタデータサイドカーを作成します。 ブラウザで直接実行するハンズオンコードでMachine Learning Academyを演習し、24時間対応のAIチューターがレッスンを進める中での質問に答えます。

Machine Learning Academyを始めるのに経験は必要ですか？

事前経験は必要ありません。CoddyKitのMachine Learning Academyは初級者から上級者向けに構成されているため、ここから始めるか最初から始めて、自分のペースで進むことができます。 これはレッスン2/4です。

「モデルのバージョン管理：ファイル名とメタデータが重要な理由」レッスンにはどのくらい時間がかかりますか？

ほとんどのCoddyKitレッスンは約5～10分かかります。各レッスンはコンパクトでインタラクティブなので、着実に進歩し、ウェブとアプリ全体で正確に前回の場所から再開できます。

このMachine Learning Academyレッスンでコードを書いて実行できますか？

はい。すべてのMachine Learning Academyレッスンに組み込みコードエディタが含まれているため、ブラウザでリアルコードを書いて実行し、即座のAIフィードバックを取得できます。ローカル設定は不要です。

このコースのすべてのレッスン

  1. joblibとpickleによるモデルの保存
  2. モデルのバージョン管理：ファイル名とメタデータが重要な理由
  3. FastAPIエンドポイントによる予測の提供
  4. 予測の監視：入力と出力のロギング
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