课程大纲
介绍
- Machine Learning 模型与传统软件的对比
DevOps 工作流程概述
Machine Learning 工作流程概述
ML 即代码加数据
ML 系统的组件
案例研究:销售 Forecasting 应用程序
Access 数据
验证数据
数据转换
从 Data Pipeline 到 ML Pipeline
构建数据模型
训练模型
验证模型
重现模型训练
部署模型
将经过训练的模型提供给生产环境
测试 ML 系统
持续交付编排
监视模型
数据版本控制
调整、扩展和维护 MLOps 平台
故障 排除
总结和结论
要求
- 了解软件开发周期
- 具有构建或使用机器学习模型的经验
- 熟悉 Python 编程
观众
- 机器学习工程师
- DevOps 工程师
- 数据工程师
- 基础设施工程师
- 软件开发人员
客户评论 (3)
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
课程 - MLflow
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
Guillaume Gautier - OLEA MEDICAL | Improved diagnosis for life™
课程 - Kubeflow
使用 DevOps 工具链
Kesh - Vodacom
课程 - DevOps Foundation®
机器翻译