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所在平台: Udemy |
课程主页: https://www.udemy.com/course/mlops-course/
课程评论：没有评论
课程名称：MLOps基础 - 学习MLOps概念与Azure演示 概述：本课程旨在教授MLOps的基础知识，包括Azure演示部分，以展示端到端MLOps项目的操作。课程详细讲解了Azure MLOps管道中涉及的所有代码。"MLOps是一种文化，包含了一系列在机器学习领域内用于顺利实施和生产化机器学习模型的原则和指导方针。"虽然数据科学家们长期以来一直在实验机器学习模型，但为了提供真正的商业价值，它们必须部署到生产环境中。不幸的是，由于当前机器学习生命周期中的挑战和缺乏系统化管理，80%的模型并未投入生产，而是停留在学术实验阶段。机器学习运维（MLOps）作为解决该问题的新兴文化，正在迅速发展，涵盖了将机器学习模型部署到生产所需的所有内容。根据业内技术讨论，2024年将是MLOps的年份，并将成为企业机器学习项目的必要技能。 课程内容包括： - MLOps的核心基础和基本概念。 - 传统机器学习生命周期管理中面临的挑战。 - MLOps如何解决这些问题，并在机器学习流程中提供更多的灵活性和自动化。 - MLOps所基于的标准和原则。 - MLOps中的持续集成（CI）、持续交付（CD）和持续训练（CT）管道。 - 与MLOps相关的各种成熟度水平。 - MLOps工具栈及MLOps平台的比较。 - Azure机器学习组件的快速入门课程。 - 在Azure中使用Azure DevOps和Azure机器学习进行的端到端CI/CD MLOps管道案例研究。
Important Note: The intention of this course is to teach MLOps fundamentals. Azure demo section is included to show the working of an end-to-end MLOps project. All the codes involved in Azure MLOps pipeline are well explained though."MLOps is a culture with set of principles, guidelines defined in machine learning world for smooth implementation and productionization of Machine learning models."Data scientists have been experimenting with Machine learning models from long time, but to provide the real business value, they must be deployed to production. Unfortunately, due to the current challenges and non-systemization in ML lifecycle, 80% of the models never make it to production and remain stagnated as an academic experiment only.Machine Learning Operations (MLOps), emerged as a solution to the problem, is a new culture in the market and a rapidly growing space that encompasses everything required to deploy a machine learning model into production.As per the tech talks in market, 2024 is the year of MLOps and would become the mandate skill set for Enterprise Machine Learning projects.What's included in the course ?MLOps core basics and fundamentals.What were the challenges in the traditional machine learning lifecycle management.How MLOps is addressing those issues while providing more flexibility and automation in the ML process.Standards and principles on which MLOps is based upon.Continuous integration (CI), Continuous delivery (CD) and Continuous training (CT) pipelines in MLOps.Various maturity levels associated with MLOps.MLOps tools stack and MLOps platforms comparisons.Quick crash course on Azure Machine learning components.An end-to-end CI/CD MLOps pipeline for a case study in Azure using Azure DevOps & Azure Machine learning.
