Course Code: dfinetunedmp
Duration: 21 hours
Prerequisites:
  • 对机器学习工作流程有深入的理解
  • 微调 ML 模型的经验
  • 熟悉 DevOps 或 MLOps 原则

观众

  • DevOps 名工程师
  • MLOps 从业者
  • AI 部署专家
Overview:

Deploying Fine-Tuned Models in Production 重点介绍将微调模型集成到可扩展的实际应用程式中所需的实用策略和工具。参与者将学习优化模型的性能、确保监控和安全性，以及解决生产环境中的挑战。

这种由讲师指导的现场培训（在线或现场）面向希望可靠、高效地部署微调模型的高级专业人员。

在本次培训结束时，参与者将能够：

  • 了解将微调模型部署到生产环境中所面临的挑战。
  • 使用 Docker 和 Kubernetes 等工具容器化和部署模型。
  • 对已部署的模型实施监控和日志记录。
  • 在实际场景中优化模型以实现延迟和可扩充性。

课程形式

  • 互动讲座和讨论。
  • 大量的练习和练习。
  • 在即时实验室环境中动手实施。

课程自定义选项

  • 要申请本课程的定制培训，请联系我们进行安排。
Course Outline:

生产部署简介

  • 部署微调模型的主要挑战
  • 开发环境和生产环境之间的差异
  • 用于模型部署的工具和平台

准备用于部署的模型

  • 以标准格式（ONNX、TensorFlow SavedModel 等）导出模型
  • 优化模型的延迟和输送量
  • 在边缘案例和真实数据上测试模型

用于模型部署的容器化

  • Docker 简介
  • 为 ML 模型建立 Docker 个图像
  • 容器安全性和效率的最佳实践

使用 Kubernetes 扩展 Deployment

  • 适用于 AI 工作负载的 Kubernetes 简介
  • 为模型托管设置 Kubernetes 集群
  • 负载均衡和水平扩展

模型监控与维护

  • 使用 Prometheus 和 Grafana 实现监控
  • 用于错误跟踪和性能的自动记录
  • 针对模型漂移和更新的重新训练管道

确保生产安全

  • 保护用于模型推理的 API
  • 身份验证和授权机制
  • 解决数据隐私问题

案例研究和动手实验

  • 部署情感分析模型
  • 扩展机器翻译服务
  • 实现对图像分类模型的监视

总结和后续步骤

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