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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deploy-machine-learning-model/
课程评论：没有评论
课程名称：在GCP与AWS Lambda上部署机器学习模型（Docker） 课程概述：本课程是关于机器学习和深度学习模型部署的实用课程，适合希望在生产环境中部署模型的学习者。您将学习如何将经过严格训练的模型序列化并部署到服务器上。完成此课程后，您将能够在云服务器上部署模型，提升您的机器学习技能，增强简历竞争力。 课程内容： 1. 课程介绍：讲解模型部署的基本概念、机器学习系统设计工作流程及不同的云端部署选项。 2. Flask快速入门：针对不熟悉Flask框架的学习者，让您了解如何使用Python的Flask进行模型部署。 3. 使用Flask进行模型部署：学习如何序列化和反序列化scikit-learn模型，并在Flask基础上创建Web服务，利用Postman和Python requests模块进行API测试。 4. 序列化深度学习Tensorflow模型：学习如何在Fashion MNIST数据集上序列化和反序列化keras模型。 5. 在Heroku云上部署：将之前序列化的花卉分类模型部署到Heroku云上。 6. 在Google云上部署：学习如何在Google云服务（如Google Cloud Function、Google App Engine和Google管理的AI云）上部署模型。 7. 在Amazon AWS Lambda上部署：将花卉分类模型部署到AWS Lambda函数。 8. 使用Docker容器在Amazon AWS ECS上部署：学习如何将应用放入Docker容器，部署到Amazon ECS（弹性容器服务）。 该课程提供30天无理由退款保证。欢迎今天就注册，快来学习吧！
Disclaimer:This course requires you to download Anaconda and Docker Desktop from their official websites. If you are a Udemy Business user, please check with your employer before downloading any software to ensure compliance with your organization's policies.Hello everyone, welcome to one of the most practical course on Machine learning and Deep learning model deployment production level.What is model deployment:Let's say you have a model after doing some rigorous training on your data set. But now what to do with this model. You have tested your model with testing data set that's fine. You got very good accuracy also with this model. But real test will come when live data will hit your model. So This course is about How to serialize your model and deployed on server.After attending this course:you will be able to deploy a model on a cloud server. You will be ahead one step in a machine learning journey.You will be able to add one more machine learning skill in your resume.What is going to cover in this course?1. Course IntroductionIn this section I will teach you about what is model deployment basic idea about machine learning system design workflow and different deployment options are available at a cloud level.2. Flask Crash courseIn this section you will learn about crash course on flask for those of you who is not familiar with flask framework as we are going to deploy model with the help of this flask web development framework available in Python.3. Model Deployment with FlaskIn this section you will learn how to Serialize and Deserialize scikit-learn model and will deploy owner flask based Web services. For testing Web API we will use Postman API testing tool and Python requests module.4. Serialize Deep Learning Tensorflow Model In this section you will learn how to serialize and deserialize keras model on Fashion MNIST Dataset.5. Deploy on Heroku cloudIn this section you will learn how to deploy already serialized flower classification data set model which we have created in a last section will deploy on Heroku cloud - Pass solution.6. Deploy on Google cloudIn this section you will learn how to deploy model on different Google cloud services like Google Cloud function, Google app engine and Google managed AI cloud.7. Deploy on Amazon AWS LambdaIn this section, you will learn how to deploy flower classification model on AWS lambda function.8. Deploy on Amazon AWS ECS with Docker ContainerIn This section, we will see how to put application inside docker container and deploy it inside Amazon ECS (Elastic Container Services)This course comes with 30 days money back guarantee. No question ask. So what are you waiting for just enroll it today.I will see you inside class.Happy learningAnkit Mistry
