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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deployment-of-machine-learning-models/
课程评论：没有评论
**课程名称：** 机器学习模型部署 **课程概述：** 本课程是关于机器学习模型部署最全面的在线课程。它将指导您如何将机器学习模型从研究环境迁移到完整的生产环境，实现模型的实际应用价值。 **什么是模型部署？** 模型部署，即“模型上线”，是指将训练好的机器学习模型提供给组织内部的其他系统或网络使用，使其能够接收输入数据并返回预测结果。通过模型部署，您可以充分发挥已构建模型的效用。 **课程适合人群：** * 刚构建出第一个机器学习模型，希望了解如何将其部署到生产环境或API的初学者。 * 已在组织内部署过模型，希望学习模型部署最佳实践的开发者。 * 渴望深入了解如何进行完整机器学习流水线部署的软件开发者。 **您将学到什么：** 本课程将引导您一步步完成从研究环境的模型创建，到将Jupyter Notebook转换为生产代码，打包代码并部署为API，以及添加持续集成和持续交付（CI/CD）的全过程。您将深入理解模型复现性的概念及其重要性，并学习如何通过版本控制、代码仓库和Docker来最大化部署过程中的模型复现性。此外，课程还将介绍可用于部署机器学习模型的工具和平台。 **具体学习内容：** * 典型机器学习流水线的步骤 * 数据科学家在研究环境中的工作方式 * 如何将Jupyter Notebook中的代码转化为生产代码 * 如何编写生产代码（包括测试、日志记录和面向对象编程介绍） * 如何部署模型并从API提供预测服务 * 如何创建Python软件包 * 如何部署到真实的生产环境 * 如何使用Docker管理软件和模型版本 * 如何添加CI/CD层 * 如何验证已部署模型与研究环境模型的一致性 **学习目标：** 完成课程后，您将对机器学习模型的整个研究、开发和部署生命周期有一个全面的认识，掌握最佳编码实践以及将模型投入生产所需考虑的因素。同时，您将更深入地了解可用于模型部署的各种工具，为根据组织需求部署模型打下坚实基础。 **其他说明：** 本课程将帮助您迈出将模型投入生产的第一步，涵盖从Jupyter Notebook到完整部署的流程，并涉及CI/CD和云平台部署。然而，模型监控、Kubernetes高级部署编排、Airflow定时工作流以及影子部署等更高级的模型部署主题不在此课程范围内。 **课程亮点：** * 超过100个视频讲座，总计约10小时的视频内容。 * 所有主题均包含可参考和复用的Python实战代码示例。 * 每个章节均设有实践作业，帮助您巩固所学并部署新模型。 **立即加入，学习如何将您的模型投入生产，并开始挖掘其真正的价值！**
Welcome to Deployment of Machine Learning Models, the most comprehensive machine learning deployments online course available to date. This course will show you how to take your machine learning models from the research environment to a fully integrated production environment.What is model deployment?Deployment of machine learning models, or simply, putting models into production, means making your models available to other systems within the organization or the web, so that they can receive data and return their predictions. Through the deployment of machine learning models, you can begin to take full advantage of the model you built.Who is this course for?If you've just built your first machine learning models and would like to know how to take them to production or deploy them into an API,If you deployed a few models within your organization and would like to learn more about best practices on model deployment,If you are an avid software developer who would like to step into deployment of fully integrated machine learning pipelines,this course will show you how.What will you learn?We'll take you step-by-step through engaging video tutorials and teach you everything you need to know to start creating a model in the research environment, and then transform the Jupyter notebooks into production code, package the code and deploy to an API, and add continuous integration and continuous delivery. We will discuss the concept of reproducibility, why it matters, and how to maximize reproducibility during deployment, through versioning, code repositories and the use of docker. And we will also discuss the tools and platforms available to deploy machine learning models.Specifically, you will learn:The steps involved in a typical machine learning pipelineHow a data scientist works in the research environmentHow to transform the code in Jupyter notebooks into production codeHow to write production code, including introduction to tests, logging and OOPHow to deploy the model and serve predictions from an APIHow to create a Python PackageHow to deploy into a realistic production environmentHow to use docker to control software and model versionsHow to add a CI/CD layerHow to determine that the deployed model reproduces the one created in the research environmentBy the end of the course you will have a comprehensive overview of the entire research, development and deployment lifecycle of a machine learning model, and understood the best coding practices, and things to consider to put a model in production. You will also have a better understanding of the tools available to you to deploy your models, and will be well placed to take the deployment of the models in any direction that serves the needs of your organization.What else should you know?This course will help you take the first steps towards putting your models in production. You will learn how to go from a Jupyter notebook to a fully deployed machine learning model, considering CI/CD, and deploying to cloud platforms and infrastructure.But, there is a lot more to model deployment, like model monitoring, advanced deployment orchestration with Kubernetes, and scheduled workflows with Airflow, as well as various testing paradigms such as shadow deployments that are not covered in this course.Want to know more? Read on...This comprehensive course on deployment of machine learning models includes over 100 lectures spanning about 10 hours of video, and ALL topics include hands-on Python code examples which you can use for reference and re-use in your own projects.In addition, we have now included in each section an assignment where you get to reproduce what you learnt to deploy a new model.So what are you waiting for? Enroll today, learn how to put your models in production and begin extracting their true value.
