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所在平台: Udemy |
课程主页: https://www.udemy.com/course/katonic-mlops-certification-course/
课程评论：没有评论
课程名称：Katonic MLOps认证课程 课程概述：机器学习运维（MLOps）提供了一种端到端的机器学习开发流程，旨在设计、构建和管理可重复、可测试和可演化的机器学习驱动的软件。MLOps是一套旨在促进数据科学家与运营专业人士之间协作和沟通的实践。实施这些实践可以提高质量，简化管理流程，并在大规模生产环境中自动化机器学习模型的部署。这门课程将介绍MLOps的概念和最佳实践，包括部署、评估、监控和操作生产机器学习系统的相关内容。 课程内容包括： - MLOps概念 - 机器学习系统的生命周期 - 模型生产化的活动 - MLOps成熟度水平 - Docker简介 - 容器、虚拟机和Pods的概念 - Kubernetes概述 - 使用命名空间 - MLOps堆栈要求 - MLOps环境概览 - AI模型生命周期 - Katonic MLOps平台介绍 - 端到端示例用例演示 - 创建工作区、获取数据和使用笔记本 - 构建机器学习管道、注册和部署模型 - 使用Streamlit构建应用程序 - 调度管道运行 - 模型监控 - 模型再训练 完成这门课程后，学员将能够： - 理解Kubernetes、Docker和MLOps的概念 - 认识到机器学习模型部署中面临的挑战及MLOps在AI运营中的关键作用 - 设计一个端到端的机器学习生产系统 - 开发原型、部署、监控并持续改进生产级别的机器学习应用程序。
Machine Learning Operations (MLOps) provides an end-to-end machine learning development process to design, build and manage reproducible, testable, and evolvable ML-powered software.It is a set of practices for collaboration and communication between data scientists and operations professionals. Deploying these practices increases the quality, simplifies the management process, and automates the deployment of Machine Learning models in large-scale production environments.With this course, get introduced to MLOps concepts and best practices for deploying, evaluating, monitoring and operating production ML systems.This course covers the following topics:What is MLOps?Lifecycle of an ML SystemActivities to Productionize a ModelMaturity Levels in MLOpsWhat is Docker?What are Containers, Virtual Machines and Pods?What is Kubernetes?Working with NamespacesMLOps Stack RequirementsMLOps LandscapeAI Model LifecycleIntroduction to Katonic MLOps PlatformEnd-to-End use case walkthroughCreating a workspaceFetching data and working with notebooks.Building an ML pipelineRegistering & deploying a modelBuilding an app using StreamlitScheduling a pipeline runModel MonitoringRetraining a modelBy the end of this course, you will be able to:Understand the concepts of Kubernetes, Docker and MLOps.Realize the challenges faced in ML model deployments and how MLOps plays a key role in operationalizing AI.Design an end-to-end ML production system.Develop a prototype, deploy, monitor and continuously improve a production-sized ML application.
