AI怎么跟得上一个不断变化的大型项目

import os
from git import Repo
from mlflow import log_artifact, log_metric, start_run

# 克隆或拉取最新代码
repo = Repo.clone_from('https://github.com/your-repo.git', 'local-repo')
repo.remotes.origin.pull()

# 训练模型
def train_model():
    # 模拟训练过程
    model = "trained_model.pkl"
    with open(model, 'wb') as f:
        f.write(b"dummy model data")
    return model

# 自动化测试
def test_model(model):
    # 模拟测试过程
    assert os.path.exists(model), "Model file does not exist"
    print("Model test passed")

# 部署模型
def deploy_model(model):
    # 模拟部署过程
    print(f"Deploying model: {model}")
    # 将模型文件上传到MLflow
    log_artifact(model)
    log_metric("deploy_status", 1)

if __name__ == "__main__":
    with start_run():
        model = train_model()
        test_model(model)
        deploy_model(model)

import mlflow
from mlflow.tracking import MlflowClient

# 初始化MLflow客户端
client = MlflowClient()

# 记录模型版本
def log_model_version(model_path, version_info):
    with mlflow.start_run() as run:
        mlflow.log_artifact(model_path)
        mlflow.set_tag("version", version_info)
        mlflow.log_param("training_data", "data_v1.csv")
        mlflow.log_param("hyperparameters", {"learning_rate": 0.01, "epochs": 100})

# 获取模型版本
def get_model_version(run_id):
    run = client.get_run(run_id)
    return run.data.tags["version"]

# 示例
model_path = "trained_model_v1.pkl"
version_info = "v1.0.0"
log_model_version(model_path, version_info)

run_id = "your_run_id"
print(f"Model version: {get_model_version(run_id)}")

import unittest
from sklearn.metrics import accuracy_score
import pandas as pd

# 模拟测试用例
class TestModel(unittest.TestCase):
    def test_accuracy(self):
        # 加载测试数据
        test_data = pd.read_csv("test_data.csv")
        X_test = test_data.drop("label", axis=1)
        y_test = test_data["label"]

        # 加载模型
        model = load_model("trained_model.pkl")

        # 预测
        y_pred = model.predict(X_test)

        # 计算准确率
        acc = accuracy_score(y_test, y_pred)
        self.assertGreaterEqual(acc, 0.8, "Model accuracy is below 80%")

# 监控脚本
def monitor_model_performance(model, data_stream):
    while True:
        # 从数据流中获取新数据
        new_data = next(data_stream)
        X_new = new_data.drop("label", axis=1)
        y_new = new_data["label"]

        # 预测
        y_pred = model.predict(X_new)

        # 计算准确率
        acc = accuracy_score(y_new, y_pred)
        if acc < 0.8:
            print(f"Warning: Model accuracy dropped to {acc:.2f}")

if __name__ == "__main__":
    unittest.main()

import pandas as pd
from prefect import task, Flow

# 数据采集任务
@task
def extract_data():
    return pd.read_csv("raw_data.csv")

# 数据处理任务
@task
def transform_data(data):
    # 模拟数据处理
    data["new_feature"] = data["feature1"] * data["feature2"]
    return data

# 数据加载任务
@task
def load_data(data):
    data.to_csv("processed_data.csv", index=False)

# 构建数据管道
with Flow("Data Pipeline") as flow:
    raw_data = extract_data()
    processed_data = transform_data(raw_data)
    load_data(processed_data)

# 运行数据管道
flow.run()