Course Code: clmusftm
Duration: 14 hours
Prerequisites:
  • 了解机器学习工作流程与神经网络架构
  • 具备模型微调与部署管道的经验
  • 熟悉数据版本控制与模型生命周期管理

目标受众

  • AI维护工程师
  • MLOps工程师
  • 负责模型生命周期连续性的机器学习从业者
Overview:

持续学习是一套策略，使机器学习模型能够逐步更新并随著时间适应新数据。

这门由讲师指导的培训（线上或线下）面向高级AI维护工程师和MLOps专业人士，他们希望为已部署且微调的模型实施稳健的持续学习管道和有效的更新策略。

在本培训结束时，参与者将能够：

  • 设计并实施已部署模型的持续学习工作流程。
  • 通过适当的训练和记忆管理来减轻灾难性遗忘。
  • 根据模型漂移或数据变化自动化监控和更新触发机制。
  • 将模型更新策略整合到现有的CI/CD和MLOps管道中。

课程形式

  • 互动式讲座和讨论。
  • 大量练习和实践。
  • 在实时实验室环境中进行实际操作。

课程定制选项

  • 如需为本课程定制培训，请联系我们进行安排。
Course Outline:

持续学习简介

  • 持续学习的重要性
  • 维护微调模型面临的挑战
  • 关键策略与学习类型（线上、增量、迁移）

数据处理与串流管道

  • 管理不断变化的数据集
  • 使用小批次与串流API进行线上学习
  • 随时间变化的数据标签与注解挑战

防止灾难性遗忘

  • 弹性权重整合（EWC）
  • 重播方法与排练策略
  • 正则化与记忆增强网络

模型漂移与监控

  • 检测数据与概念漂移
  • 模型健康与性能衰减的指标
  • 触发自动模型更新

模型更新的自动化

  • 自动重新训练与排程策略
  • 与CI/CD和MLOps工作流的整合
  • 管理更新频率与回滚计划

持续学习框架与工具

  • Avalanche、Hugging Face Datasets与TorchReplay概览
  • 平台对持续学习的支持（例如MLflow、Kubeflow）
  • Scala能力与部署考量

真实世界Use Case与架构

  • 基于演化模式的客户行为预测
  • 工业机器监控与增量改进
  • 变化威胁模型下的诈欺检测系统

总结与下一步

Sites Published:

United Arab Emirates - Continual Learning and Model Update Strategies for Fine-Tuned Models

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