AI-Native 部署与发布：智能交付流水线

# 传统部署：代码不变，行为不变
def calculate_discount(price, user_type):
    if user_type == "VIP":
        return price * 0.8
    return price

# AI-Native部署：同样的模型，不同的行为
recommendation = model.predict(user_features)  # 输出随输入分布变化而变化

# intelligent-risk-controller.py
class IntelligentRiskController:
    def __init__(self):
        self.detectors = {
            # 技术指标
            'latency': LatencyAnomalyDetector(),
            'error_rate': ErrorRateDetector(),
            'throughput': ThroughputDetector(),
            
            # 业务指标
            'conversion_rate': BusinessMetricDetector(),
            'user_engagement': EngagementDetector(),
            'revenue_impact': RevenueImpactDetector(),
            
            # AI特有指标
            'model_drift': ModelDriftDetector(),
            'prediction_confidence': ConfidenceScoreDetector(),
            'output_distribution': DistributionShiftDetector(),
        }
    
    def evaluate(self, metrics_window):
        """
        AI综合风险评估
        返回: (risk_score, should_act, recommended_action)
        """
        # 多维度异常信号聚合
        anomaly_signals = []
        for name, detector in self.detectors.items():
            signal = detector.analyze(metrics_window)
            anomaly_signals.append(signal)
        
        # 使用轻量级模型评估整体风险
        risk_score = self.risk_model.predict(anomaly_signals)
        
        # 决策逻辑
        if risk_score > 0.8:
            return risk_score, True, "immediate_rollback"
        elif risk_score > 0.5:
            return risk_score, True, "traffic_throttle"
        elif risk_score > 0.3:
            return risk_score, True, "enhanced_monitoring"
        else:
            return risk_score, False, "continue"

# 当检测到轻微异常时，AI自动调节流量
if risk_score == 0.6:  # 中等风险
    # 不是简单的全量回滚，而是智能限流
    new_traffic_percent = max(5, current_percent * 0.5)
    adjust_traffic(new_traffic_percent)
    
    # 同时增加监控密度
    increase_metrics_collection(frequency=2x)
    
    # 启动根因分析
    trigger_automated_analysis()

# multi-dimensional-anomaly-detection.py
class MultidimensionalAnomalyDetector:
    """
    不只监控技术指标，还监控业务指标和AI模型指标
    """
    
    def __init__(self):
        self.metric_categories = {
            'technical': {
                'latency': {'p50': 50, 'p99': 200, 'weight': 0.15},
                'error_rate': {'threshold': 0.001, 'weight': 0.15},
                'throughput': {'drop_threshold': 0.1, 'weight': 0.1},
            },
            'business': {
                'conversion_rate': {'drop_threshold': 0.02, 'weight': 0.2},
                'revenue_per_user': {'drop_threshold': 0.03, 'weight': 0.15},
                'churn_rate': {'increase_threshold': 0.01, 'weight': 0.1},
            },
            'ai_specific': {
                'prediction_confidence': {'min_avg': 0.7, 'weight': 0.08},
                'output_entropy': {'max_increase': 0.3, 'weight': 0.05},
                'model_drift_score': {'max': 0.2, 'weight': 0.02},
            }
        }
    
    def detect_anomaly(self, canary_metrics, baseline_metrics):
        """
        使用统计方法和机器学习检测异常
        """
        anomalies = []
        
        for category, metrics in self.metric_categories.items():
            for metric_name, config in metrics.items():
                canary_value = canary_metrics.get(metric_name)
                baseline_value = baseline_metrics.get(metric_name)
                
                # 统计异常检测
                is_anomaly = self.statistical_test(
                    canary_value, baseline_value, config
                )
                
                # 机器学习异常检测（捕捉非线性模式）
                ml_anomaly_score = self.ml_detector.predict(
                    metric_name, canary_value, baseline_value
                )
                
                if is_anomaly or ml_anomaly_score > 0.7:
                    anomalies.append({
                        'metric': metric_name,
                        'category': category,
                        'severity': self.calculate_severity(
                            canary_value, baseline_value, config
                        ),
                        'weight': config['weight']
                    })
        
        # 综合风险评分
        total_risk = sum(a['severity'] * a['weight'] for a in anomalies)
        
        return {
            'is_anomaly': total_risk > 0.5,
            'risk_score': total_risk,
            'anomalies': anomalies
        }

# intelligent-canary-recommendation.py
class RecommendationCanary:
    def evaluate_promotion(self, canary_stats):
        """
        针对推荐系统的特殊评估逻辑
        """
        # 不仅看技术指标，还要看推荐质量
        signals = {
            'technical_health': self.check_tech_metrics(canary_stats),
            'recommendation_quality': self.estimate_quality(canary_stats),
            'user_engagement': self.analyze_engagement(canary_stats),
            'business_impact': self.project_business_impact(canary_stats),
        }
        
        # 使用代理模型预测全量效果
        predicted_conversion = self.impact_model.predict(
            current_stats=canary_stats,
            target_traffic=1.0
        )
        
        if predicted_conversion < baseline_conversion * 0.98:
            return {
                'decision': 'halt',
                'reason': f'预测转化率下降 {(1-predicted_conversion/baseline_conversion)*100:.1f}%',
                'confidence': 0.87
            }
        
        return {'decision': 'proceed', 'confidence': 0.92}
    
    def estimate_quality(self, stats):
        """
        使用代理指标评估推荐质量
        """
        # 点击率、收藏率、加购率的综合评估
        engagement_score = (
            stats.click_through_rate * 0.4 +
            stats.add_to_cart_rate * 0.4 +
            stats.wishlist_rate * 0.2
        )
        return engagement_score

# bayesian-ab-testing.py
from scipy import stats

class BayesianABTest:
    """
    贝叶斯A/B测试：持续更新对版本表现的信念
    """
    
    def __init__(self, variants):
        self.variants = {
            v: {'alpha': 1, 'beta': 1, 'conversions': 0, 'trials': 0}
            for v in variants
        }
    
    def update(self, variant, converted):
        """观察到一个结果后更新后验分布"""
        v = self.variants[variant]
        v['trials'] += 1
        if converted:
            v['conversions'] += 1
            v['alpha'] += 1
        else:
            v['beta'] += 1
    
    def recommend_allocation(self):
        """
        Thompson采样：根据表现概率分配流量
        表现越好的版本获得越多流量
        """
        allocations = {}
        for variant, params in self.variants.items():
            # 从Beta分布采样（Thompson Sampling）
            sampled_rate = stats.beta.rvs(params['alpha'], params['beta'])
            allocations[variant] = sampled_rate
        
        # 归一化为流量分配比例
        total = sum(allocations.values())
        return {v: a/total for v, a in allocations.items()}
    
    def probability_best(self, variant):
        """计算某个版本是最佳版本的概率"""
        # 蒙特卡洛估计
        n_samples = 10000
        samples = {
            v: stats.beta.rvs(p['alpha'], p['beta'], size=n_samples)
            for v, p in self.variants.items()
        }
        
        wins = np.sum(samples[variant] >= 
                     np.max([samples[v] for v in self.variants if v != variant], axis=0))
        return wins / n_samples

# contextual-bandit-deployment.py
class ContextualBanditDeployer:
    """
    根据用户特征动态选择最优版本
    """
    
    def __init__(self):
        # 为不同用户群体学习最优版本
        self.models = {
            'new_user': LinearBanditModel(),
            'returning_user': LinearBanditModel(),
            'premium_user': LinearBanditModel(),
        }
    
    def select_version(self, user_context):
        """
        基于用户上下文选择版本
        """
        user_segment = self.classify_user(user_context)
        model = self.models[user_segment]
        
        # 探索 vs 利用权衡
        if random.random() < self.exploration_rate:
            # 探索：随机选择以收集数据
            return random.choice(self.versions)
        else:
            # 利用：选择预期奖励最高的版本
            expected_rewards = {
                v: model.predict_reward(user_context, v)
                for v in self.versions
            }
            return max(expected_rewards, key=expected_rewards.get)
    
    def update(self, user_context, version, reward):
        """根据实际结果更新模型"""
        user_segment = self.classify_user(user_context)
        self.models[user_segment].update(user_context, version, reward)

# prompt-ab-test-automation.py
class PromptABTestFramework:
    """
    自动化Prompt A/B测试与优化
    """
    
    def __init__(self):
        self.metrics = ['response_quality', 'latency', 'cost', 'user_satisfaction']
    
    def run_experiment(self, prompt_a, prompt_b, test_dataset):
        """
        自动化对比两个Prompt
        """
        results = {'A': [], 'B': []}
        
        for sample in test_dataset:
            # 并行调用两个版本
            response_a = self.call_with_prompt(prompt_a, sample)
            response_b = self.call_with_prompt(prompt_b, sample)
            
            # AI评估响应质量
            quality_a = self.ai_judge.evaluate(sample, response_a)
            quality_b = self.ai_judge.evaluate(sample, response_b)
            
            results['A'].append(quality_a)
            results['B'].append(quality_b)
        
        # 统计分析
        winner, confidence = self.statistical_comparison(results['A'], results['B'])
        
        return {
            'winner': winner,
            'confidence': confidence,
            'effect_size': self.cohens_d(results['A'], results['B']),
            'recommendation': self.generate_recommendation(winner, confidence)
        }
    
    def auto_optimize(self, base_prompt, optimization_goal):
        """
        使用AI自动优化Prompt
        """
        # 生成候选Prompt变体
        variants = self.prompt_engineer.generate_variants(
            base_prompt, 
            n_variants=5,
            optimization_target=optimization_goal
        )
        
        # 并行测试所有变体
        results = self.run_multi_variant_test(variants)
        
        # 返回最优Prompt
        best_variant = max(results, key=lambda x: x['expected_value'])
        return best_variant['prompt']

# intelligent-rollback-triggers.py
class RollbackTriggerEngine:
    """
    多维度智能回滚触发器
    """
    
    def __init__(self):
        self.triggers = [
            # 硬性指标（必须回滚）
            HardTrigger(
                name='system_availability',
                condition=lambda m: m.error_rate > 0.05,  # 5%错误率
                action='immediate_rollback',
                severity='critical'
            ),
            
            # 业务指标（可能回滚）
            BusinessTrigger(
                name='revenue_impact',
                condition=lambda m: m.revenue_drop_5min > 10000,  # 5分钟损失>$10k
                action='gradual_rollback',
                severity='high',
                require_confirmation=True
            ),
            
            # AI预测性触发（提前回滚）
            PredictiveTrigger(
                name='failure_prediction',
                condition=lambda m: m.predicted_failure_probability > 0.7,
                action='preemptive_rollback',
                severity='medium'
            ),
            
            # 复合条件（多个轻微异常叠加）
            CompositeTrigger(
                name='death_by_thousand_cuts',
                conditions=[
                    lambda m: m.latency_p99 > baseline * 1.5,
                    lambda m: m.error_rate > baseline * 2,
                    lambda m: m.cpu_usage > 0.8,
                ],
                threshold=2,  # 满足2个即触发
                action='investigate_and_prepare_rollback'
            )
        ]

# rollback-impact-assessment.py
class RollbackImpactAssessor:
    """
    评估回滚可能带来的影响
    """
    
    def assess(self, current_version, target_version):
        """
        综合分析回滚影响
        """
        impacts = {
            'user_experience': self.assess_ux_impact(),
            'data_consistency': self.assess_data_impact(),
            'business_continuity': self.assess_business_impact(),
            'technical_debt': self.assess_technical_impact()
        }
        
        return {
            'total_impact_score': self.calculate_total_impact(impacts),
            'breakdown': impacts,
            'recommendation': self.generate_recommendation(impacts),
            'safe_rollback_procedure': self.design_safe_procedure(impacts)
        }
    
    def assess_data_impact(self):
        """
        检查数据兼容性问题
        """
        # 检查schema变更
        schema_diff = self.compare_schemas(current_version, target_version)
        
        # 检查数据格式变更
        data_format_diff = self.compare_data_formats()
        
        # 检查进行中的事务
        in_flight_transactions = self.count_in_flight_transactions()
        
        return {
            'schema_compatible': len(schema_diff) == 0,
            'data_loss_risk': self.calculate_data_loss_risk(),
            'grace_period_needed': max(30, in_flight_transactions * 2),
            'recommendations': self.generate_data_recommendations()
        }