概述
ML 模型部署服务工作流是一个高级难度的工作流。部署训练好的 ML 模型为服务端点，包含 A/B 测试、监控和回滚能力。该工作流的目标是：Deploy a machine learning model to production with monitoring and rollback.
环境准备
执行本工作流需要以下环境：A trained ML model artifact；A model serving framework；A monitoring system for model performance。请确保在开始前准备好所有必需的工具和资源，以避免中途因环境问题中断。
执行步骤
本工作流包含5个步骤。步骤1：Package the model——Serialize the trained model and its dependencies into a deployable artifact. 步骤2：Deploy the endpoint——Deploy the model as a serving endpoint with the serving framework. 步骤3：Set up A/B testing——Route a percentage of traffic to the new model for comparison against the current model. 步骤4：Monitor performance——Track prediction latency, error rate, and model accuracy metrics. 步骤5：Full rollout or rollback——Compare A/B results and fully deploy the new model or roll back if metrics regress.
验证与清理
预期结果：A model is deployed, A/B tested, and either fully rolled out or rolled back based on metrics.。验证方法：Send test predictions and confirm the model responds correctly; verify A/B routing works as configured.。清理操作：Roll back to the previous model if prediction quality regresses; keep the model artifact for analysis.。estimatedMinutes 为 90 分钟。
代码示例
# Execute the workflow steps as documented above
echo "Workflow completed successfully"