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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deploy-serverless-machine-learning-models-to-aws-lambda/
课程评论：没有评论
课程名称：将无服务器机器学习模型部署到AWS Lambda 概述：本课程将引导您发现将各种机器学习模型快速、高效、经济地部署到生产环境的一种可扩展方式，利用无服务器计算的原则。一旦您将训练好的机器学习模型部署到云端，本课程将以AWS为例，服务提供商将负责管理服务器基础设施、自动扩展、监控、安全更新和日志记录。您将使用免费的AWS资源，这些资源足以支撑您完成整个课程。如果您用完了这些资源，这种情况非常不常见，您仅需为实际使用量付费。 通过本课程的讲座，您将学习到亚马逊网络服务（Amazon Web Services），特别是Lambda、API Gateway、S3、CloudWatch等内容。课程还将介绍多种现实生活中的用例，这些用例部署了不同类型的机器学习模型，例如自然语言处理（NLP）、深度学习计算机视觉或回归模型。我们将使用不同的机器学习框架——scikit-learn、spaCy、Keras/TensorFlow，并展示如何将它们准备好以适应AWS Lambda。此外，您还将了解易于使用且高效的无服务器框架，它使得Lambda的创建和部署变得非常简单。 虽然本课程并不主要关注训练和微调机器学习模型的技术，但会有一些在Jupyter Notebook中训练模型和使用预训练模型的示例。
In this course you will discover a very scalable, cost-effective and quick way of deploying various machine learning models to production by using principles of serverless computing. Once when you deploy your trained ML model to the cloud, the service provider (AWS in this course) will take care of managing server infrastructure, automated scaling, monitoring, security updating and logging. You will use free AWS resources which are enough for going through the entire course. If you spend them, which is very unlikely, you will pay only for what you use.By following course lectures, you will learn about Amazon Web Services, especially Lambda, API Gateway, S3, CloudWatch and others. You will be introduced with various real-life use cases which deploy different kinds of machine learning models, such as NLP, deep learning computer vision or regression models. We will use different ML frameworks - scikit-learn, spaCy, Keras / Tensorflow - and show how to prepare them for AWS Lambda. You will also be introduced with easy-to-use and effective Serverless Framework which makes Lambda creation and deployment very easy.Although this course doesn't focus much on techniques for training and fine-tuning machine learning models, there will be some examples of training the model in Jupyter Notebook and usage of pre-trained models.
