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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/mlops-fundamentals
课程评论：没有评论
课程名称：MLOps（机器学习运营）基础 课程概述：本课程向参与者介绍用于在Google Cloud上部署、评估、监控和操作生产机器学习系统的MLOps工具和最佳实践。MLOps是一种专注于机器学习系统在生产环境中部署、测试、监控和自动化的学科。机器学习工程师利用工具实现已部署模型的持续改进和评估。他们与开发模型的数据科学家合作，以加快和严格地部署性能最佳的模型。 课程目标群体： - 希望迅速将机器学习原型转化为生产，以带来商业影响的数据科学家。 - 希望发展机器学习工程技能的软件工程师。 - 希望在其机器学习生产项目中采用Google Cloud的机器学习工程师。 课程大纲： 1. 欢迎来到机器学习运营（MLOps）：入门 - 该模块提供课程概述。 2. 应用机器学习运营 - 本模块识别机器学习从业者的痛点，并探讨机器学习中的DevOps概念。您将了解机器学习生命周期的三个阶段以及如何自动化机器学习过程。 3. Vertex AI与MLOps在Vertex AI上的应用 - 本模块探讨Vertex AI的概念及统一平台的重要性。 4. 总结 - 该模块提供课程内容的总结。 通过注册本课程，您同意遵守Qwiklabs服务条款，详情请参考FAQ中的相关链接。
Name:Welcome to the Machine Learning Operations (MLOps): Getting Started
Description:This module provides the overview of the course
Name:Employing Machine Learning Operations
Description:This module identifies ML practitioners' pain points before exploring the concept of DevOps in ML. You're introduced to the three phases of the ML lifecycle and automating the ML process.
Name:Vertex AI and MLOps on Vertex AI
Description:This module explores what Vertex AI is and why a unified platform matters.
Name:Summary
Description:Summary
This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models. This course is primarily intended for the following participants: Data Scientists looking to quickly go from machine learning prototype to production to deliver business impact. Software Engineers looking to develop Machine Learning Engineering skills. ML Engineers who want to adopt Google Cloud for their ML production projects. >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<
