使用Streamlit部署机器学习模型

使用Streamlit部署ML模型，并与世界分享您的数据科学成果，使用Streamlit部署机器学习模型的完整课程。构建由ML和AI支持的web应用程序，并部署它们以与世界共享。本课程将带您从基础到部署由机器学习支持的可扩展应用。为了检验你的知识，我已经设计了超过六个带有完整指导解决方案的顶点项目。本课程包括:简化和交互元素的基础，如按钮、表单、滑块、输入元素等。显示图表自定义应用程序的布局顶点项目:构建交互式仪表板缓存缓存缓存的性能增强缓存的基本和高级用法顶点项目:部署分类模型会话状态管理通过会话状态管理添加更多交互性并提高性能会话状态的基本和高级用法顶点项目:部署回归模型多页面应用程序构建具有多个页面的大型应用程序顶点项目:训练和排列分类模型身份验证添加具有身份验证的安全层添加登录/注销组件高级身份验证用户管理、重置密码等。顶点项目:为营销部署群集模型连接到数据源连接到数据库通过API访问数据顶点项目:部署销售需求模型部署部署a & nbsp利用机密管理和环境变量简化it应用程序的免费高级部署流程，Machine Learning Model Deployment with Streamlit

由Marco Peixeiro创作
MP4 |视频:h264，1280×720 |音频:AAC，44.1 KHz，2声道
类型:电子学习|语言:英语|时长:44节课(7小时13分钟)|大小:2.58 GB

你会学到什么
了解Streamlit的核心概念和功能
构建交互式数据驱动的web应用程序来部署您的模型
掌握Streamlit中的高级功能和集成
为Streamlit应用最佳实践和优化技术
将您的Streamlit应用程序连接到数据源
免费部署您的Streamlit应用程序

要求
需要Python和机器学习的工作知识。
本课程仅关注使用Streamlit部署模型。我们不会花时间解释模型是如何工作的，或者它们是如何被开发和训练的。
安装了Anaconda的计算机。
安装了您最喜欢的文本编辑器(我使用VSCode)

Deploy ML models with Streamlit and share your data science work with the world

What you’ll learn
Understand the core concepts and features of Streamlit
Build interactive data-driven web applications to deploy your model
Master the advanced features and integrations in Streamlit
Apply the best practices and optimization techniques for Streamlit
Connect your Streamlit app to data sources
Deploy your Streamlit app for free

Requirements
A working knowledge of Python and machine learning is required.
This course focuses only on deploying models using Streamlit. We will not spend time explaining how the models work or how they are developed and trained.
A computer with Anaconda installed.
Your favourite text editor installed (I use VSCode)

Description
The complete course to deploy machine learning models using Streamlit. Build web applications powered by ML and AI and deploy them to share them with the world.This course will take you from the basics to deploying scalable applications powered by machine learning. To put your knowledge to the test, I have designed more than six capstone projects with full guided solutions.This course covers:Basics of StreamlitAdd interactive elements, like buttons, forms, sliders, input elements, etc.Display chartsCustomize the layout of your application Capstone project: build an interactive dashboardCachingPerformance enhancement with cachingBasic and advanced usage of cachingCapstone project: deploy a classification modelSession state managementAdd more interactivity and boost performance with session state managementBasic and advanced usage of session stateCapstone project: deploy a regression modelMultipage applicationsBuild large apps with multiple pagesCapstone project: train and rank classification modelsAuthenticationAdd a security layer with authenticationAdd login/logout componentsAdvanced authentication with user management, reset password, etc.Capstone project: deploy a clustering model for marketingConnect to data sourcesConnect to databasesAccess data through APIsCapstone project: Deploy a sales demand modelDeploymentDeploy a Streamlit app for freeAdvanced deployment process with secrets management and environment variables