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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deploy-data-science-nlp-models-with-docker-containers/
课程评论：没有评论
课程名称：为商业部署人工智能与机器学习模型（Python） 概述：机器学习作为当今行业中的新兴热门词汇，被广泛应用于各个商业领域，以提供数据驱动的解决方案，解决复杂的业务问题。然而，将由机器学习技术构建的解决方案顺利部署，以便于各商业单位使用，而不是孤立操作，是一项挑战。本课程由来自全球的UNP精英数据科学家团队创建，旨在聚焦数据科学家和计算解决方案架构师在行业中所面临的挑战，精炼为一句话：“我有了机器学习模型，它运行正常！接下来该怎么办？”本课程将帮助您建立数据科学的基础知识，并学习如何通过Docker容器部署这些解决方案，使您的模型能够作为服务（API）供所有需要的用户使用。 在课程结束时，您将能够： - 学习Docker、Docker文件和Docker容器的基本知识 - 学习Flask基础知识及应用程序接口（API） - 构建随机森林模型并进行部署 - 构建基于自然语言处理的测试聚类模型（K-Means）并进行可视化 - 构建图片处理和识别的API，并在其背后使用深度学习模型（卷积神经网络：CNN） 本课程是数据科学基础、行业标准、机器学习的更广泛理解和实际应用的完美结合，特别是在模型部署方面。
Machine Learning, as we know it is the new buzz word in the industry today. This is practiced in every sector of business imaginable to provide data-driven solutions to complex business problems. This poses the challenge of deploying the solution, built by the Machine Learning technique so that it can be used across the intended Business Unit and not operated in silos.This is an extensive and well-thought course created & designed by UNP's elite team of Data Scientists from around the world to focus on the challenges that are being faced by Data Scientists and Computational Solution Architects across the industry which is summarized the below sentence: "I HAVE THE MACHINE LEARNING MODEL, IT IS WORKING AS EXPECTED!! NOW, WHAT ?????" This course will help you create a solid foundation of the essential topics of data science along with a solid foundation of deploying those created solutions through Docker containers which eventually will expose your model as a service (API) which can be used by all who wish for it.At the end of this course, you will be able to: Learn about Docker, Docker Files, Docker ContainersLearn Flask Basics & Application Program Interface (API)Build a Random Forest Model and deploy it.Build a Natural Language Processing based Test Clustering Model (K-Means) and visualize it.Build an API for Image Processing and Recognition with a Deep Learning Model under the hood (Convolutional Neural Network: CNN) This course is a perfect blend of foundations of data science, industry standards, broader understanding of machine learning and practical applications and most importantly deploying them.
