- 精通 Python 程式设计
- 了解机器学习工作流程
- 熟悉容器化工具，如 Docker
- 具有 DevOps 种实践经验（推荐）
观众
- MLOps 工程师
- DevOps 专业人士
将 AI 代理部署到生产环境中是操作 AI 模型并确保其在实际应用程式中的可扩充性、可靠性和性能的关键步骤。
这种讲师指导的现场培训（在线或现场）面向希望掌握在生产环境中部署和管理 AI 代理的技术的高级专业人员。
在本次培训结束时，参与者将能够：
- 设计和实施可扩展的 AI 部署管道。
- 使用 Docker 和 Kubernetes 等工具容器化和编排 AI 代理。
- 监控和优化生产环境中的 AI 代理。
- 为 AI 代理部署实施 CI/CD 工作流。
- 确保符合安全和数据管理要求。
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。
AI 部署简介
- AI 部署生命周期概述
- 将 AI 代理部署到生产环境的挑战
- 关键注意事项：可伸缩性、可靠性和可维护性
容器化和编排
- Docker 和容器化基础知识简介
- 使用 Kubernetes 进行 AI 代理编排
- 管理容器化 AI 应用程式的最佳实践
提供 AI 模型
- 模型服务框架概述（例如，TensorFlow Serving、TorchServe）
- 构建用于 AI 代理推理的 REST API
- 处理批量预测与实时预测
AI Agents 的 CI/CD
- 为 AI 部署设置 CI/CD 管道
- 自动化 AI 模型的测试和验证
- 滚动更新和管理版本控制
监控和优化
- 实施 AI 代理性能的监控工具
- 分析模型漂移和重新训练需求
- 优化资源利用率和可扩充性
安全和 Go生存
- 确保遵守数据隐私法规
- 保护 AI 部署管道和 API
- AI 应用程式的审计和日志记录
动手实践 Activities
- 使用 Docker 容器化 AI 代理
- 使用 Kubernetes 部署 AI 代理
- 设置 AI 性能和资源使用情况监控
总结和后续步骤
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