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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/managing-machine-learning-projects
课程评论：没有评论
课程名称：管理机器学习项目 概述：本课程是杜克大学普拉特工程学院人工智能产品管理专业的第二门课程，重点关注管理机器学习项目的实际方面。课程将逐步讲解机器学习项目的关键步骤，包括如何识别适合机器学习的机会、数据收集、模型构建、部署以及生产系统的监控和维护。参与者将学习数据科学流程，并了解如何将该流程应用于组织机器学习工作，以及在设计机器学习系统时的关键考虑和决策。 完成本课程后，您将能够： 1) 识别可利用机器学习解决用户问题的机会 2) 应用数据科学流程来组织机器学习项目 3) 评估机器学习系统设计中的关键技术决策 4) 从构思到生产，使用最佳实践领导机器学习项目 课程大纲： 1. 识别机器学习机会：讨论值得解决的问题、确定机器学习是否适合作为解决方案的一部分及验证解决方案概念的过程，并探讨启发式方法在建模项目中的实用性及相对优劣。 2. 组织机器学习项目：重点关注CRISP-DM数据科学流程及其在组织机器学习项目中的应用，理解机器学习项目相较于普通软件项目的独特性，并讨论管理机器学习项目固有风险的方法，同时走访机器学习项目团队中的关键角色及工作的组织方式。 3. 数据考虑：探讨机器学习项目中出现的关键数据相关问题，讨论数据的来源、清理和特征集的开发与选择等关键考虑因素，以及确保数据管道可重复性的最佳实践。 4. 机器学习系统设计与技术选择：讨论设计机器学习系统时的重要决策，如云计算与边缘计算、在线与批处理的比较，以及机器学习项目中需要做出的主要技术决策，介绍构建机器学习模型的常用工具与技术。 5. 模型生命周期管理：最后一个模块聚焦于识别和缓解机器学习模型投入生产后所面临的关键问题，讨论如何建立强大的模型监控能力，定义维护计划以保持生产模型的高性能，并强调版本控制在机器学习系统中的重要性，以促进部署后的快速迭代。
Name:Identifying Opportunities for Machine Learning
Description:In this module we will discuss how to identify problems worth solving, how to determine whether ML is a good fit as part of the solution, and how to validate solution concepts. We will also learn why heuristics are useful in modeling projects and the advantages and disadvantages of ML relative to heuristics.
Name:Organizing ML Projects
Description:In this module we will focus on the CRISP-DM data science process and how it can be used to organize ML projects. We will begin by understanding what is unique about ML project relative to normal software projects, and then discuss approaches to manage the inherent risks of ML projects. We will also walk through the key roles on a ML project team and how to organize work.
Name:Data Considerations
Description:In this module we will explore the key data-related issues that arise in ML projects. Data is the foundation of successful machine learning, and gathering data of sufficient quantity and quality with the right set of attributes is the key to a successful project. We will discuss the key considerations in sourcing data, cleaning data, and developing and selecting a feature set to use in modeling. The module will conclude with a discussion on best practices to ensure reproducibility of your data pipeline.
Name:ML System Design & Technology Selection
Description:In this module we will discuss the key decisions to make in designing ML systems, such as cloud vs. edge and online vs. batch, and compare the benefits of each type of system. We will then discuss the primary technology decisions to make in a ML project and introduce the common tools and technologies used to build ML models.
Name:Model Lifecycle Management
Description:The final module in the course focuses on identifying and mitigating the key issues which ML models experience once they are in production. We will discuss how to set up a robust ML system monitoring capability and define a model maintenance plan to maintain high performance of a production model. We will conclude with a discussion on the importance of versioning in ML systems to facilitate continued rapid iteration even after deployment.
This second course of the AI Product Management Specialization by Duke University's Pratt School of Engineering focuses on the practical aspects of managing machine learning projects. The course walks through the keys steps of a ML project from how to identify good opportunities for ML through data collection, model building, deployment, and monitoring and maintenance of production systems. Participants will learn about the data science process and how to apply the process to organize ML efforts, as well as the key considerations and decisions in designing ML systems. At the conclusion of this course, you should be able to: 1) Identify opportunities to apply ML to solve problems for users 2) Apply the data science process to organize ML projects 3) Evaluate the key technology decisions to make in ML system design 4) Lead ML projects from ideation through production using best practices
