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模型版本管理：文件名与元数据为何重要

您将设计一种命名规范，将训练日期、数据集版本和指标分数嵌入文件名，并编写 JSON 元数据附属文件以支持治理。

模型版本管理：文件名与元数据为何重要 是 CoddyKit 上的免费 Machine Learning Academy 课时。这是第 2 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 Machine Learning Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Machine Learning Academy 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

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.

常见问题解答

「模型版本管理：文件名与元数据为何重要」课时是免费的吗？

是的 — 「模型版本管理：文件名与元数据为何重要」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Machine Learning Academy 课程的其余内容，请升级到 CoddyKit PRO。 Machine Learning Academy 课程共包含 4 节课。

「模型版本管理：文件名与元数据为何重要」这节课中我会学到什么？

您将设计一种命名规范，将训练日期、数据集版本和指标分数嵌入文件名，并编写 JSON 元数据附属文件以支持治理。你通过在浏览器中直接运行的动手代码来练习 Machine Learning Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

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