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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/cloud-machine-learning-engineering-mlops-duke
课程评论：没有评论
课程名称：云机器学习工程与MLOps 概述：欢迎参加构建大规模云计算解决方案专项中的第四门课程！在本课程中，您将基于前三门课程中介绍的云计算和数据工程概念，将机器学习工程应用于实际项目。首先，您将开发机器学习工程应用程序，并使用软件开发最佳实践来创建这些应用。接着，您将学习如何利用AutoML解决问题，这比传统机器学习方法更加高效。最后，您将深入了解机器学习中的新兴主题，包括MLOps、边缘机器学习和AI APIs。 本课程非常适合初学者和希望将云计算应用于数据科学、机器学习和数据工程的中级学生。学生应具备初级Linux和中级Python技能。在本课程的项目中，您将构建一个Flask网络应用程序，提供机器学习预测服务。 课程大纲： 1. 开始机器学习工程 - 本周，您将学习机器学习工程中的相关方法。到本周结束时，您将能够开发机器学习工程应用程序，并使用软件开发最佳实践创建这些应用。 2. 使用AutoML - 本周，您将学习AutoML及其如何帮助您以较少或无需编码的方式构建高效的机器学习解决方案。您将接触到多个工具，包括Ludwig、Google AutoML、Apple Create ML和Azure机器学习工作室，并应用开源和云AutoML技术。 3. 机器学习中的新兴主题 - 本周，您将学习MLOps战略和设计云解决方案的最佳实践，探讨边缘机器学习和AI API的使用。您将应用这些策略构建一个低代码或无代码的云解决方案，执行自然语言处理或计算机视觉任务。
Name:Getting Started with Machine Learning Engineering
Description:This week, you will learn about the methodologies involved in Machine Learning Engineering. By the end of the week, you will be able to develop Machine Learning Engineering applications and use software development best practices to create Machine Learning Engineering applications.
Name:Using AutoML
Description:This week, you will learn about AutoML and how to use it to build efficient Machine Learning solutions with little to no code. These technologies include Ludwig, Google AutoML, Apple Create ML and Azure Machine Learning Studio. You will apply these solutions by using both open source and Cloud AutoML technology.
Name:Emerging Topics in Machine Learning
Description:This week, you will learn MLOps strategies and best practices in designing Cloud solutions. Then, you will explore Edge Machine Learning and how to use AI APIs. You will apply these strategies to build a low code or no code Cloud solution that performs Natural Language Processing or Computer Vision.
Welcome to the fourth course in the Building Cloud Computing Solutions at Scale Specialization! In this course, you will build upon the Cloud computing and data engineering concepts introduced in the first three courses to apply Machine Learning Engineering to real-world projects. First, you will develop Machine Learning Engineering applications and use software development best practices to create Machine Learning Engineering applications. Then, you will learn to use AutoML to solve problems more efficiently than traditional machine learning approaches alone. Finally, you will dive into emerging topics in Machine Learning including MLOps, Edge Machine Learning and AI APIs. This course is ideal for beginners as well as intermediate students interested in applying Cloud computing to data science, machine learning and data engineering. Students should have beginner level Linux and intermediate level Python skills. For your project in this course, you will build a Flask web application that serves out Machine Learning predictions.
