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所在平台: Udemy |
课程主页: https://www.udemy.com/course/sagemaker/
课程评论：没有评论
课程名称：AWS Sagemaker 2018 - 完全托管的机器学习服务 课程概述： Amazon SageMaker 是一种完全托管的机器学习服务。通过 Amazon SageMaker，数据科学家和开发人员能够快速且轻松地构建和训练机器学习模型，并将其直接部署到生产环境中。该服务提供集成的 Jupyter 创作笔记本实例，使用户可以方便地访问数据源进行探索和分析，无需管理服务器。此外，SageMaker 提供的常用机器学习算法经过优化，能够在分布式环境中高效处理极大数据量。它本土支持自定义算法和框架，提供灵活的分布式训练选项，以适应特定的工作流程。用户可以通过在 Amazon SageMaker 控制台单击一下，将模型部署到安全且可扩展的环境中。训练和托管按使用分钟计费，无最低费用和预付款承诺。 课程内容： 如果您想了解 Amazon SageMaker，本课程将详细介绍其工作原理。课程将概述 Amazon SageMaker，解释关键概念，并描述与之相关的核心组件以构建 AI 解决方案。建议按顺序学习此主题。课程还说明如何设置账户并创建第一个 Amazon SageMaker 笔记本实例。 课程将引导学员进行模型训练练习，使用 SageMaker 提供的训练算法。根据需求，课程涵盖以下主题： - 提交 Python 代码以使用深度学习框架进行训练 - 从 Apache Spark 直接使用 Amazon SageMaker - 利用 Amazon AI 训练和/或部署自定义算法 - 使用 Docker 打包自定义算法，以便在 Amazon SageMaker 中进行训练和/或部署 本课程还包含更多内容，适合希望深入了解 AWS Sagemaker 的学习者。
Amazon SageMaker is a fully managed machine learning service. With Amazon SageMaker, data scientists and developers can quickly and easily build and train machine learning models, and then directly deploy them into a production-ready hosted environment. It provides an integrated Jupyter authoring notebook instance for easy access to your data sources for exploration and analysis, so you don't have to manage servers. It also provides common machine learning algorithms that are optimized to run efficiently against extremely large data in a distributed environment. With native support for bring-your-own-algorithms and frameworks, Amazon SageMaker offers flexible distributed training options that adjust to your specific workflows. Deploy a model into a secure and scalable environment by launching it with a single click from the Amazon SageMaker console. Training and hosting are billed by minutes of usage, with no minimum fees and no upfront commitments.If you want to learn about Amazon SageMaker, I recommend you to go through this course which will cover in detail- How it works? This course provides an overview of Amazon SageMaker, explains key concepts, and describes the core components involved in building AI solutions with Amazon SageMaker. We recommend that you read this topic in the order presented.This course explains how to set up your account and create your first Amazon SageMaker notebook instance.Try a model training exercise - This course walks you through training your first model. You use training algorithms provided by Amazon SageMaker. Explore other topics here- Depending on your needs, the following:Submit Python code to train with deep learning frameworks - In Amazon SageMaker, you can use your own TensorFlow or Apache MXNet scripts to train models. Use Amazon SageMaker directly from Apache Spark Use Amazon AI to train and/or deploy your own custom algorithms - Package your custom algorithms with Docker so you can train and/or deploy them in Amazon SageMaker. And a ton, more....is included in this course..
