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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/introduction-to-machine-learning-in-production
课程评论：没有评论
课程名称：机器学习生产入门 概述：在机器学习工程生产专业化的第一门课程中，您将识别构建端到端机器学习（ML）生产系统的各种组件。这包括项目范围、数据需求、建模策略以及部署约束和要求；同时学习如何确定模型基线、解决概念漂移，以及原型化开发、部署和持续改进生产化机器学习应用程序的流程。理解机器学习和深度学习的概念是必不可少的，但如果您希望在人工智能领域建立有效的职业生涯，您还需要具备生产工程能力。机器学习工程为生产结合了机器学习的基础概念与现代软件开发和工程角色的功能专业知识，帮助您发展出适合生产的技能。 课程大纲： 第一周：机器学习生命周期和部署概述 本周内容快速介绍机器学习生产系统，重点关注其需求和挑战。同时，讨论部署生产系统的必要条件，确保在处理不断变化的数据时的稳健性。 第二周：建模挑战与策略 本周探讨建模策略以及模型开发中的主要挑战。内容包括错误分析和使用不同数据类型的策略，同时讨论如何应对类别不平衡和高度偏斜的数据集。 第三周：数据定义与基线设定 本周专注于处理不同的数据类型，并确保分类问题中标签的一致性。最终确定您的模型性能基线，并讨论在时间与资源限制下提高模型性能的策略。本周还包括最终的端到端项目。 通过这门课程，您将获得构建和部署机器学习模型的实用技能，并为未来的机器学习工程职业打下坚实基础。
Name:Week 1: Overview of the ML Lifecycle and Deployment
Description:This week covers a quick introduction to machine learning production systems focusing on their requirements and challenges. Next, the week focuses on deploying production systems and what is needed to do so robustly while facing constantly changing data.
Name:Week 2: Modeling Challenges and Strategies
Description:This week is about model strategies and key challenges in model development. It covers error analysis and strategies to work with different data types. It also addresses how to cope with class imbalance and highly skewed data sets.
Name:Week 3: Data Definition and Baseline
Description:This week is all about working with different data types and ensuring label consistency for classification problems. This leads to establishing a performance baseline for your model and discussing strategies to improve it given your time and resources constraints. This week also includes the final end-to-end project.
In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Overview of the ML Lifecycle and Deployment Week 2: Selecting and Training a Model Week 3: Data Definition and Baseline
