予測の監視：入力と出力のロギング
APIに予測ログを追加し、データドリフトとモデルの陳腐化について考察するとともに、再学習のトリガーとデプロイパイプラインの概要を設計します。
「予測の監視：入力と出力のロギング」はCoddyKit上の無料Machine Learning Academyレッスンです。 これはレッスン4/4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。
Why Monitor a Deployed Model?
A model that performed excellently at deployment can silently degrade as the real world changes. Customer behaviour shifts, new product categories appear, sensors drift, economic conditions change — all causing the input distribution to diverge from training data. Without monitoring, you discover model failure when users complain or business metrics drop. Prediction monitoring catches degradation early, before it impacts users.
What to Log: The Three W's
Every prediction event should log: When (timestamp), What (input features and the model's output/prediction/probability), and ideally Whether (the eventual ground truth label, once available). The input features are needed for data drift detection; the outputs enable confidence monitoring; the ground truth enables accuracy monitoring over time.
Adding Logging to the FastAPI Endpoint
Extend the FastAPI prediction route to log each request to a structured JSONL file (JSON Lines — one JSON object per line). JSONL is easy to append to, easy to parse, and works well with tools like pandas, Spark, and cloud log aggregators. Each log line is one prediction event.
import json
from datetime import datetime
from fastapi import FastAPI
from pydantic import BaseModel
import numpy as np
app = FastAPI()
LOG_FILE = '/tmp/prediction_log.jsonl'
def log_prediction(features: dict, prediction: int, confidence: float):
entry = {
'timestamp': datetime.utcnow().isoformat(),
'features': features,
'predicted_class': prediction,
'confidence': confidence
}
with open(LOG_FILE, 'a') as f:
f.write(json.dumps(entry) + '\n')
# Call inside the predict route:
# log_prediction(features.dict(), pred, confidence)
print('Logging enabled — entries written to', LOG_FILE)Simulating and Reading the Log
After accumulating predictions, read the JSONL file into a pandas DataFrame for analysis. Each row is a prediction event. You can compute statistics over any time window: rolling mean confidence, feature distribution shifts, and prediction class frequency.
import json
import pandas as pd
from datetime import datetime
import random
# Simulate log entries
LOG_FILE = '/tmp/prediction_log.jsonl'
with open(LOG_FILE, 'w') as f:
for i in range(100):
entry = {
'timestamp': datetime.utcnow().isoformat(),
'features': {'sepal_length': round(4.5 + random.gauss(1, 0.5), 2),
'sepal_width': round(3.0 + random.gauss(0, 0.3), 2),
'petal_length': round(1.0 + random.gauss(2, 1), 2),
'petal_width': round(0.2 + random.gauss(0.5, 0.2), 2)},
'predicted_class': random.choice([0, 1, 2]),
'confidence': round(random.uniform(0.6, 1.0), 4)
}
f.write(json.dumps(entry) + '\n')
# Read back as DataFrame
rows = [json.loads(line) for line in open(LOG_FILE)]
df = pd.json_normalize(rows)
print(df.shape)
print(df[['confidence', 'predicted_class']].describe())Monitoring Confidence Over Time
Plot rolling mean confidence over time. A downward trend signals that the model is becoming less certain about its predictions — a classic early warning sign of data drift. Set a threshold (e.g., rolling confidence below 0.7) and trigger an alert. This gives you a warning before accuracy actually drops.
import pandas as pd
import matplotlib.pyplot as plt
import json
df = pd.json_normalize([json.loads(l) for l in open('/tmp/prediction_log.jsonl')])
df['timestamp'] = pd.to_datetime(df['timestamp'])
df = df.sort_values('timestamp').reset_index(drop=True)
# Rolling window of 20 predictions
df['rolling_confidence'] = df['confidence'].rolling(20).mean()
plt.figure(figsize=(10, 4))
plt.plot(df.index, df['confidence'], alpha=0.3, label='Per-prediction')
plt.plot(df.index, df['rolling_confidence'], linewidth=2, label='Rolling mean (n=20)')
plt.axhline(0.7, color='red', linestyle='--', label='Alert threshold')
plt.xlabel('Prediction number')
plt.ylabel('Confidence')
plt.title('Prediction Confidence Over Time')
plt.legend()
plt.show()Detecting Feature Distribution Shifts
Compare the distribution of each input feature in recent predictions against the training distribution. The Population Stability Index (PSI) is a common metric: PSI < 0.1 means no significant shift; 0.1–0.2 is moderate drift; >0.2 is severe drift requiring retraining. Alternatively, a Kolmogorov-Smirnov test tests whether two samples come from the same distribution.
import numpy as np
from scipy import stats
import pandas as pd
import json
df = pd.json_normalize([json.loads(l) for l in open('/tmp/prediction_log.jsonl')])
# Simulated training distribution for sepal_length
train_sepal = np.random.normal(5.8, 0.8, 500) # reference
recent_sepal = df['features.sepal_length'].values
# KS test: tests if two samples come from same distribution
stat, p_value = stats.ks_2samp(train_sepal, recent_sepal)
print(f'KS statistic: {stat:.4f} p-value: {p_value:.4f}')
if p_value < 0.05:
print('WARNING: sepal_length distribution has drifted!')
else:
print('No significant drift detected in sepal_length')Monitoring Prediction Class Frequencies
Plot the rolling frequency of each predicted class. If class 0 was predicted 60% of the time during training-period evaluation but is now being predicted only 10% of the time, something in the input data has shifted. Class distribution shifts are often easier to detect than subtle feature distribution changes and can serve as a quick first alert.
import pandas as pd
import json
df = pd.json_normalize([json.loads(l) for l in open('/tmp/prediction_log.jsonl')])
# Compute rolling class frequencies in windows of 20
window = 20
rolling_freq = pd.get_dummies(df['predicted_class']).rolling(window).mean()
rolling_freq.columns = [f'class_{c}' for c in rolling_freq.columns]
print('Recent class frequencies (last 20 predictions):')
print(rolling_freq.tail(1).to_string())
print('\nTraining class frequency (expected):')
print('class_0: 0.33 class_1: 0.33 class_2: 0.33')Structured Logging for Production
In production, write logs to a structured logging system rather than a flat file. Options include: Python logging module with JSON formatter, cloud logging services (AWS CloudWatch, GCP Cloud Logging, Datadog), or a database. Structured logs are queryable, scalable, and integrate with dashboards like Grafana for real-time monitoring.
import logging
import json
from datetime import datetime
# Configure structured JSON logging
class JSONFormatter(logging.Formatter):
def format(self, record):
log_data = {
'time': datetime.utcnow().isoformat(),
'level': record.levelname,
'message': record.getMessage()
}
if hasattr(record, 'prediction_data'):
log_data.update(record.prediction_data)
return json.dumps(log_data)
logger = logging.getLogger('ml_predictor')
handler = logging.StreamHandler()
handler.setFormatter(JSONFormatter())
logger.addHandler(handler)
logger.setLevel(logging.INFO)
# Usage in prediction route:
extra = {'prediction_data': {'features': {'x': 1.5}, 'pred': 0, 'conf': 0.95}}
logger.info('prediction', extra=extra)Data Drift vs Concept Drift
Two distinct failure modes exist: Data drift — the distribution of input features changes (e.g., a sensor recalibrates, new customer demographics emerge). Concept drift — the relationship between inputs and the target changes (e.g., what constitutes 'fraud' evolves as fraudsters adapt). Data drift can sometimes be handled by retraining on fresh data; concept drift requires rethinking features or the modelling approach.
Retraining Triggers
Define clear retraining triggers before deployment. Common triggers: scheduled (retrain every 30 days regardless), performance-based (retrain if AUC on a labelled holdout drops below a threshold), or drift-based (retrain when PSI exceeds 0.2 on any key feature). Automating the trigger and the retraining pipeline prevents the model from silently degrading.
from datetime import datetime, timedelta
class RetrainingPolicy:
def __init__(self, last_trained: datetime, max_age_days=30,
min_auc=0.85, max_drift_psi=0.2):
self.last_trained = last_trained
self.max_age_days = max_age_days
self.min_auc = min_auc
self.max_drift_psi = max_drift_psi
def should_retrain(self, current_auc=None, max_psi=None):
age = (datetime.utcnow() - self.last_trained).days
if age >= self.max_age_days:
return True, f'Model is {age} days old (max {self.max_age_days})'
if current_auc and current_auc < self.min_auc:
return True, f'AUC {current_auc:.3f} below threshold {self.min_auc}'
if max_psi and max_psi > self.max_drift_psi:
return True, f'PSI {max_psi:.3f} above threshold {self.max_drift_psi}'
return False, 'No trigger met — model healthy'
policy = RetrainingPolicy(last_trained=datetime.utcnow() - timedelta(days=35))
print(policy.should_retrain(current_auc=0.90))The Monitoring Dashboard Sketch
A production ML monitoring dashboard typically shows: rolling prediction confidence over time, class distribution over time, feature distribution histograms comparing training vs recent predictions, and (when labels are available) rolling accuracy or AUC. Tools like Grafana + Prometheus, Evidently AI, or WhyLogs provide pre-built components for ML monitoring without building from scratch.
# Evidently AI example (pip install evidently)
from evidently.report import Report
from evidently.metric_preset import DataDriftPreset
import pandas as pd
import numpy as np
# Reference: training feature distributions
reference = pd.DataFrame({
'sepal_length': np.random.normal(5.8, 0.8, 300),
'petal_length': np.random.normal(3.7, 1.7, 300)
})
# Current: recent predictions features
current = pd.DataFrame({
'sepal_length': np.random.normal(6.5, 1.2, 100), # drifted!
'petal_length': np.random.normal(3.7, 1.7, 100)
})
report = Report(metrics=[DataDriftPreset()])
report.run(reference_data=reference, current_data=current)
report.save_html('/tmp/drift_report.html')
print('Drift report saved to /tmp/drift_report.html')Quick Check
Test your understanding of prediction monitoring and logging from this lesson.
Lesson Recap
In this lesson you learned: log every prediction event with timestamp, input features, predicted class, and confidence to enable drift detection, monitor rolling confidence and feature distributions to catch degradation before it affects users, and define automated retraining triggers (schedule, performance threshold, or drift threshold) to keep the model healthy in production. This completes the Model Persistence and Deployment Basics course.
よくある質問
「予測の監視：入力と出力のロギング」レッスンは無料ですか？
はい。「予測の監視：入力と出力のロギング」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。
「予測の監視：入力と出力のロギング」で何を学びますか？
APIに予測ログを追加し、データドリフトとモデルの陳腐化について考察するとともに、再学習のトリガーとデプロイパイプラインの概要を設計します。 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4/4です。
「予測の監視：入力と出力のロギング」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。