Machine Learning Academy · 강의

모델 버전 관리: 파일 이름과 메타데이터가 중요한 이유

학습자는 학습 날짜, 데이터셋 버전 및 지표 점수를 포함하는 이름 지정 규칙을 설계하고, 거버넌스를 위한 JSON 메타데이터 보조 파일을 작성합니다.

모델 버전 관리: 파일 이름과 메타데이터가 중요한 이유은(는) CoddyKit의 무료 Machine Learning Academy 강의입니다. 이것은 4개 중 2번째 강의입니다. 아래에서 전체 강의를 무료로 읽을 수 있으며, 내장 코드 에디터와 24/7 AI 튜터와 함께 브라우저에서 직접 실습할 수 있습니다. 이 강의는 Machine Learning Academy 학습 경로의 일부이며, 진행 상황이 웹과 CoddyKit 앱에 동기화됩니다. Machine Learning Academy 강의에는 총 4개의 강의가 포함되어 있습니다.

이 강의의 일부는 아직 번역되지 않았으며 영어로 표시됩니다.

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.

자주 묻는 질문

“모델 버전 관리: 파일 이름과 메타데이터가 중요한 이유” 강의는 무료인가요?

네 — “모델 버전 관리: 파일 이름과 메타데이터가 중요한 이유” 전체 내용을 이 웹사이트에서 무료로 읽을 수 있습니다. 인터랙티브하게 실습하려면(내장 코드 에디터와 24/7 AI 튜터), CoddyKit PRO로 업그레이드하면 Machine Learning Academy 강의 전체를 잠금 해제할 수 있습니다. Machine Learning Academy 강의에는 총 4개의 강의가 포함되어 있습니다.

“모델 버전 관리: 파일 이름과 메타데이터가 중요한 이유”에서 뭘 배우나요?

학습자는 학습 날짜, 데이터셋 버전 및 지표 점수를 포함하는 이름 지정 규칙을 설계하고, 거버넌스를 위한 JSON 메타데이터 보조 파일을 작성합니다. 브라우저에서 직접 실행하는 실습 코드로 Machine Learning Academy을(를) 배우며, 24/7 AI 튜터가 강의를 진행하면서 질문에 답변해줍니다.

Machine Learning Academy을(를) 시작하는 데 경험이 필요한가요?

사전 경험은 필요하지 않습니다. CoddyKit의 Machine Learning Academy은(는) 초급자부터 고급 학습자까지를 위해 구성되어 있으므로, 여기서 시작하거나 처음부터 시작할 수 있으며 자신의 속도대로 진행할 수 있습니다. 이것은 4개 중 2번째 강의입니다.

“모델 버전 관리: 파일 이름과 메타데이터가 중요한 이유” 강의는 얼마나 걸리나요?

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