AutoML逆袭：普通开发者如何玩转大模型调参

graph TD
    A[原始特征] --> B{自动化处理引擎}
    B -->|组合运算| C[数值组合]
    B -->|时序分析| D[时空特征]
    B -->|语义理解| E[文本增强]
    C --> F["消费力指数 = 交易频率 × 金额"]
    D --> G["用户行为模式序列"]
    E --> H["BERT-文本向量"]

graph LR
    A[搜索空间] --> B(控制器)
    B --> C[子模型]
    C --> D{评估反馈}
    D -->|强化学习| B

# 腾讯云TI-ONE SDK示例
from ti import nas

searcher = nas.DARTS_Searcher(
    search_space='mobilenet_v3',
    objective='accuracy',
    constraints={'latency': 50, 'flops': 500},
    budget=24 # GPU小时数
)
best_model = searcher.run(dataset='imagenet-1k')

graph TD
    A[原始特征] --> B{自动化处理引擎}
    B -->|组合运算| C[数值组合]
    B -->|时序分析| D[时空特征]
    B -->|语义解析| E[文本增强]
    C --> F["消费力指数 = 交易频率 × 金额"]
    D --> G["用户行为序列模式"]
    E --> H["BERT-文本嵌入"]

# 腾讯云TI-ONE特征自动化组件
from ti.feature import AutoTransformer

transformer = AutoTransformer(
    n_level=3, # 特征衍生层级
    interaction=["cross", "ratio"], 
    temporal=["rolling_mean_7d", "delta_time"],
    text_embedding="bert-mini"
)
train_trans = transformer.fit_transform(raw_data)

graph LR
    A[基线MAE 15.6] --> B[一级衍生特征]
    B --> C[MAE 12.4]
    C --> D[二级交互特征]
    D --> E[MAE 9.8]
    E --> F[时序特征增强]
    F --> G[最终MAE 7.2]

graph LR
    A[Raw Data] --> B(AutoFE)
    B --> C(AutoML)
    C --> D(Model Zoo)
    D --> E(One-Click Deployment)
    E --> F[API Service]

# 腾讯云TI-ONE端到端示例
from ti.auto import AutoPipeline

pipeline = AutoPipeline(
    task='classification',
    metric='auc',
    constraints={'QPS': 100, 'max_latency': 200},
    deployment='k8s_cluster'
)
pipeline.run(data_path='s3://bucket/data.csv')

# 输出部署信息
print(f"API端点: {pipeline.endpoint}")
print(f"监控看板: {pipeline.dashboard_url}")

graph TB
    A[重复劳动] --> B(AutoML接管)
    B --> C[开发者聚焦]
    C --> D{价值创新}
    D --> E[业务理解深度]
    D --> F[系统架构设计]
    D --> G[领域知识沉淀]

# 腾讯云TI-ONE模型压缩SDK示例  
from ti.compression import AutoPruner  

pruner = AutoPruner(  
    strategy="nas+quant",  
    target_device="arm_v8",  
    latency_constraint=30,  
    accuracy_drop=0.02 # 最大允许精度损失  
)  
compressed_model = pruner.process(trained_model)  

graph TD  
    A[特征重要性TOP10] --> B{业务可解释性}  
    B -->|通过| C[进入生产环境]  
    B -->|未通过| D[人工干预]  
    D --> E[特征语义分析]  
    E --> F[生成特征报告]  
    F --> G[迭代特征生成器]  
    G --> A  

graph LR  
    A[业务理解] --> B(AutoML配置)  
    B --> C[自动优化]  
    C --> D{效果评估}  
    D -->|达标| E[部署上线]  
    D -->|未达标| F[人工分析]  
    F --> G[调整搜索空间]  
    G --> B  

graph TD
    A[数据主权] --> B{部署架构}
    B --> C[区域化模型中心]
    B --> D[联邦学习节点]
    C --> E[欧盟GDPR合规模型]
    D --> F[亚太实时推理集群]

# 全球化部署代码示例
from ti.global_deploy import CrossBorderDeploy

deployer = CrossBorderDeploy(
    regions=['eu-west', 'ap-southeast'],
    data_governance={
        'gdpr_compliance': True,
        'data_masking': {'fields': ['user_id', 'ip_address']}
    },
    model_customization={
        'eu': {'privacy_level': 4},
        'asia': {'language_pack': 'multi_lang_v2'}
    }
)
deployer.deploy(model_path='model.pb')

graph LR
    A[量子神经网络] --> B{AutoML 3.0}
    B -->|优化策略| C[超参数量子退火]
    B -->|结构探索| D[架构量子叠加态]
    B -->|特征演化| E[量子纠缠特征]
    C --> F[训练速度提升100x]
    D --> F
    E --> F