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所在平台: Udemy |
课程主页: https://www.udemy.com/course/a-to-z-nlp-machine-learning-model-building-and-deployment/
课程评论：没有评论
课程名称：从A到Z的自然语言处理（NLP）机器学习模型构建与部署。 课程概述：机器学习的真正价值在于将机器学习解决方案实际部署到生产环境中，并在此之后进行必要的监控和优化。许多人面临的问题是：我已经创建了机器学习模型，那么接下来该如何处理？如何将这个模型提供给最终用户，答案是通过API，但它是如何工作的？你如何理解Docker的作用以及如何监控我们创建的构建。该课程专门针对这些领域进行了设计，结合了行业标准的构建管道和一些最常见、最重要的工具。 课程内容包括以下几个部分： 1. 配置和快速浏览我们在课程中使用的每个工具和技术。 2. 构建我们的NLP机器学习模型并调整超参数。 3. 创建Flask API并在浏览器中运行Web API。 4. 创建Docker文件，构建镜像并在Docker容器中运行我们的机器学习模型。 5. 配置GitLab并将代码推送到GitLab。 6. 配置Jenkins，编写Jenkins文件并运行端到端集成。 该课程非常适合希望体验行业标准数据科学和在本地服务器上部署应用的学习者。希望你能享受这门课程，就像我在制作这门课程时所享受的那样。
Machine Learning Real value comes from actually deploying a machine learning solution into production and the necessary monitoring and optimization work that comes after it.Most of the problems nowadays as I have made a machine-learning model but what next.How it is available to the end-user, the answer is through API, but how it works?How you can understand where the Docker stands and how to monitor the build we created.This course has been designed to keep these areas under consideration. The combination of industry-standard build pipeline with some of the most common and important tools.This course has been designed into Following sections:1) Configure and a quick walkthrough of each of the tools and technologies we used in this course.2) Building our NLP Machine Learning model and tune the hyperparameters.3) Creating flask API and running the WebAPI in our Browser.4) Creating the Docker file, build our image and running our ML Model in Docker container.5) Configure GitLab and push your code in GitLab.6) Configure Jenkins and write Jenkins's file and run end-to-end Integration.This course is perfect for you to have a taste of industry-standard Data Science and deploying in the local server. Hope you enjoy the course as I enjoyed making it.
