2026 技术观察：MLOps 进入模型治理阶段，版本管理、漂移监测和灰度回滚成为 AI 生产新底座

import json
import random
from datetime import datetime
from collections import defaultdict


MODEL_VERSIONS = [
    {
        "model_id": "risk_model",
        "version": "1.0.0",
        "auc": 0.82,
        "precision": 0.76,
        "recall": 0.71,
        "status": "stable"
    },
    {
        "model_id": "risk_model",
        "version": "1.1.0",
        "auc": 0.86,
        "precision": 0.79,
        "recall": 0.74,
        "status": "candidate"
    }
]


ONLINE_CONFIG = {
    "model_id": "risk_model",
    "current_version": "1.0.0",
    "gray_version": "1.1.0",
    "gray_ratio": 0.1
}

def evaluate_candidate_model(current, candidate):
    improvements = {
        "auc_diff": round(candidate["auc"] - current["auc"], 4),
        "precision_diff": round(candidate["precision"] - current["precision"], 4),
        "recall_diff": round(candidate["recall"] - current["recall"], 4)
    }

    if improvements["auc_diff"] > 0.02 and improvements["precision_diff"] >= 0:
        decision = "allow_gray"
        message = "候选模型离线指标提升，可以进入灰度。"
    elif improvements["auc_diff"] < -0.01:
        decision = "reject"
        message = "候选模型离线指标下降，不建议上线。"
    else:
        decision = "manual_review"
        message = "候选模型指标变化不明显，建议人工复核。"

    return {
        "current_version": current["version"],
        "candidate_version": candidate["version"],
        "improvements": improvements,
        "decision": 30656.t.kuaisou.com 
        "message": message
    }

BASELINE_FEATURES = {
    "amount_avg": 300,
    "login_count_avg": 5,
    "device_change_rate": 0.12
}


ONLINE_FEATURES = {
    "amount_avg": 520,
    "login_count_avg": 8,
    "device_change_rate": 0.28
}


def detect_feature_drift(baseline, online):
    drift_results = []
    total_score = 0

    for feature, base_value in baseline.items():
        current_value = online.get(feature, base_value)

        diff_rate = abs(current_value - base_value) / base_value

        if diff_rate > 0.5:
            level = "high"
            total_score += 3
        elif diff_rate > 0.25:
            level = "medium"
            total_score += 2
        elif diff_rate > 0.1:
            level = "low"
            total_score += 1
        else:
            level = "normal"

        drift_results.append({
            "feature": feature,
            "baseline": base_value,
            "online": current_value,
            "diff_rate": round(diff_rate * 100, 2),
            "drift_level": level
        })

    if total_score >= 6:
        overall = "high"
    elif total_score >= 3:
        overall = "medium"
    elif total_score > 0:
        overall = "low"
    else:
        overall = "normal"

    return {
        "overall_drift": overall,
        "drift_score": total_score,
        "details": 30657.t.kuaisou.com 
    }

def monitor_gray_model_performance():
    metrics = {
        "error_rate": round(random.uniform(0.005, 0.08), 3),
        "avg_latency_ms": random.randint(40, 260),
        "business_precision": round(random.uniform(0.65, 0.85), 3),
        "complaint_rate": round(random.uniform(0.001, 0.02), 3)
    }

    issues = []
    score = 0

    if metrics["error_rate"] > 0.04:
        issues.append("灰度模型错误率偏高。")
        score += 3

    if metrics["avg_latency_ms"] > 200:
        issues.append("灰度模型响应延迟偏高。")
        score += 2

    if metrics["business_precision"] < 0.7:
        issues.append("灰度模型业务精度偏低。")
        score += 3

    if metrics["complaint_rate"] > 0.01:
        issues.append("灰度流量投诉率偏高。")
        score += 2

    if score >= 5:
        status = "bad"
    elif score >= 2:
        status = "watch"
    else:
        status = "good"

    return {
        "metrics": metrics,
        "status": status,
        "issues": issues
    }

def decide_model_release(evaluation, drift_result, gray_result):
    reasons = []

    if evaluation["decision"] == "reject":
        return {
            "decision": "block",
            "message": "候选模型未通过离线评估。",
            "reasons": [evaluation["message"]]
        }

    if drift_result["overall_drift"] == "high":
        reasons.append("线上输入数据漂移明显，建议重新评估训练数据。")

    if gray_result["status"] == "bad":
        reasons.extend(gray_result["issues"])

    if reasons:
        return {
            "decision": "rollback",
            "message": "灰度模型存在风险，建议回滚到稳定版本。",
            "reasons": reasons
        }

    if gray_result["status"] == "watch":
        return {
            "decision": "keep_gray",
            "message": "模型表现需继续观察，不建议立即全量。",
            "reasons": gray_result["issues"]
        }

    return {
        "decision": "full_release",
        "message": "模型灰度表现稳定，可以逐步全量发布。",
        "reasons": []
    }

def run_mlops_model_governance():
    current = MODEL_VERSIONS[0]
    candidate = MODEL_VERSIONS[1]

    evaluation = evaluate_candidate_model(
        current,
        candidate
    )

    drift_result = detect_feature_drift(
        BASELINE_FEATURES,
        ONLINE_FEATURES
    )

    gray_result = monitor_gray_model_performance()

    release_decision = decide_model_release(
        evaluation,
        drift_result,
        gray_result
    )

    report = {
        "report_name": "MLOps 模型治理报告",
        "online_config": ONLINE_CONFIG,
        "evaluation": evaluation,
        "drift_result": drift_result,
        "gray_result": 30658.t.kuaisou.com
        "release_decision": release_decision,
        "generate_time": datetime.now().isoformat()
    }

    return report


if __name__ == "__main__":
    report = run_mlops_model_governance()

    print(json.dumps(
        report,
        ensure_ascii=False,
        indent=2
    ))