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所在平台: Udemy |
课程主页: https://www.udemy.com/course/build-an-aws-machine-learning-pipeline-for-object-detection/
课程评论：没有评论
**课程名称：** 为目标检测构建 AWS 机器学习流水线 **课程概述：** 本课程旨在教授如何使用 Amazon SageMaker、AWS Step Functions 和 AWS Lambda 构建一个可扩展、安全且复杂的机器学习流水线，专门用于目标检测。 **核心内容：** * **AWS SageMaker 入门：** 学习如何利用 SageMaker 的全面托管服务，快速轻松地构建、训练和部署机器学习模型。内容涵盖数据预处理、模型构建和使用 SageMaker 内置算法进行训练。 * **AWS Step Functions 应用：** 掌握如何使用 Step Functions 来协调和管理机器学习流水线中的各个步骤，从而构建可扩展、安全且可靠的机器学习流水线。 * **AWS Lambda 集成：** 学习如何利用 Lambda 函数触发流水线中的不同步骤，实现自动化和精细控制。 * **深度学习与目标检测：** 深入探讨深度学习相关主题，包括如何使用神经网络进行目标检测，以及如何通过超参数调优来优化模型以适应不同的应用场景。 * **Web 应用开发：** 教授如何使用 React、Next.js、Express 和 MongoDB 构建一个 Web 应用程序，该应用可以提交数据给机器学习流水线，查看结果并跟踪作业进度。 **课程目标：** 通过本课程的学习，学员将能够： * 深刻理解如何使用 SageMaker、Step Functions 和 Lambda 函数构建可扩展、安全且复杂的机器学习流水线。 * 掌握使用这些 AWS 服务进行数据预处理、模型训练、超参数调优和部署的技能。 * 具备使用前端技术（React, Next.js）和后端技术（Express, MongoDB）构建与机器学习流水线交互的 Web 应用程序的能力。 * 获得将机器学习模型应用于实际问题，解决现实世界挑战的实践经验。
Welcome to the ultimate course on creating a scalable, secure, complex machine learning pipeline with Sagemaker, Step Functions, and Lambda functions. In this course, we will cover all the necessary steps to create a robust and reliable machine learning pipeline, from data preprocessing to hyperparameter tuning for object detection.We will start by introducing you to the basics of AWS Sagemaker, a fully-managed service that provides developers and data scientists with the ability to build, train, and deploy machine learning models quickly and easily. You will learn how to use Sagemaker to preprocess and prepare your data for machine learning, as well as how to build and train your own machine learning models using Sagemaker's built-in algorithms.Next, we will dive into AWS Step Functions, which allow you to coordinate and manage the different steps of your machine learning pipeline. You will learn how to create a scalable, secure, and robust machine learning pipeline using Step Functions, and how to use Lambda functions to trigger your pipeline's different steps.In addition, we will cover deep learning related topics, including how to use neural networks for object detection, and how to use hyperparameter tuning to optimize your machine learning models for different use cases.Finally, we will walk you through the creation of a web application that will interact with your machine learning pipeline. You will learn how to use React, Next.js, Express, and MongoDB to build a web app that will allow users to submit data to your pipeline, view the results, and track the progress of their jobs.By the end of this course, you will have a deep understanding of how to create a scalable, secure, complex machine learning pipeline using Sagemaker, Step Functions, and Lambda functions. You will also have the skills to build a web app that can interact with your pipeline, opening up new possibilities for how you can use your machine learning models to solve real-world problems.
