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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/build-and-operate-machine-learning-solutions-with-azure
课程评论：没有评论
课程名称：使用 Azure 构建和运营机器学习解决方案 课程概述：Azure 机器学习是一个云平台，用于训练、部署、管理和监控机器学习模型。在本课程中，您将学习如何使用 Azure 机器学习 Python SDK 创建和管理企业级机器学习解决方案。本课程是一个五门课程项目中的第三门，旨在帮助您准备 DP-100: 设计和实施 Azure 上的数据科学解决方案认证考试。 认证考试是证明您在云规模上使用 Azure 机器学习操作机器学习解决方案的知识和专业技能的机会。本专业化课程教您如何利用您现有的 Python 和机器学习知识来管理数据摄取与准备、模型训练与部署，以及机器学习解决方案监控。每门课程都教授与考试相关的概念和技能。 本专业化课程面向已具备 Python 和类似 Scikit-Learn、PyTorch 和 Tensorflow 等机器学习框架知识的数据科学家，旨在帮助他们在云中构建和运营机器学习解决方案。课程内容包括如何在 Microsoft Azure 中创建端到端解决方案。学生将学习如何管理 Azure 机器学习的资源；运行实验和训练模型；部署和运营机器学习解决方案，以及实施负责任的机器学习。他们还将学习使用 Azure Databricks 探索、准备和建模数据，并将 Databricks 的机器学习流程与 Azure 机器学习集成。 课程大纲： 1. **使用 Azure 机器学习 SDK 训练模型**：学习如何配置 Azure 机器学习工作区，使用工具和接口进行代码实验，并使用 Azure 机器学习训练模型并注册到工作区。 2. **在 Azure 机器学习中处理数据和计算**：学习如何处理 Azure 机器学习中的数据存储和数据集，以构建可扩展的云基础模型训练解决方案，并在 Azure 机器学习中使用云计算运行大规模训练实验。 3. **编排管道并使用 Azure 机器学习部署实时机器学习服务**：学习如何创建、发布和运行管道来训练模型，以及如何使用 Azure 机器学习服务来注册和部署机器学习模型。 4. **部署批量推断管道并使用 Azure 机器学习调整超参数**：学习如何使用 Azure 机器学习发布批量推断管道，并利用云规模实验选择模型训练的最佳超参数值。 5. **选择模型并保护敏感数据**：学习如何使用 Azure 机器学习中的自动机器学习来找到您的数据的最佳模型，以及如何通过差分隐私保护可识别的个人数据。 6. **监控机器学习部署**：学习如何使用 Fairlearn 和 Azure 机器学习检测和减轻模型中的不公平性，利用遥测了解模型在生产中的使用情况，以及监控数据漂移以确保模型持续准确预测。
Name:Use the Azure Machine Learning SDK to train a model
Description:Azure Machine Learning provides a cloud-based platform for training, deploying, and managing machine learning models. In this module, you will learn how to provision an Azure Machine Learning workspace. You will use tools and interfaces to work with Azure Machine Learning and run code-based experiments in an Azure Machine Learning workspace. finally, you will learn how to use Azure Machine Learning to train a model and register it in a workspace.
Name:Work with Data and Compute in Azure Machine Learning
Description:Data is the foundation of machine learning. In this module, you will learn how to work with datastores and datasets in Azure Machine Learning, enabling you to build scalable, cloud-based model training solutions. You'll also learn how to use cloud compute in Azure Machine Learning to run training experiments at scale.
Name:Orchestrate pipelines and deploy real-time machine learning services with Azure Machine Learning
Description:Orchestrating machine learning training with pipelines is a key element of DevOps for machine learning. In this module, you'll learn how to create, publish, and run pipelines to train models in Azure Machine Learning. You'll also learn how to register and deploy ML models with the Azure Machine Learning service.
Name:Deploy batch inference pipelines and tune hyperparameters with Azure Machine Learning
Description:Machine learning models are often used to generate predictions from large numbers of observations in a batch process. You will accomplish this using Azure Machine Learning to publish a batch inference pipeline. You will also leverage cloud-scale experiments to choose optimal hyperparameter values for model training.
Name:Select models and protect sensitive data
Description:In this module, you will learn how to use automated machine learning in Azure Machine Learning to find the best model for your data. You will learn how differential privacy is a leading edge approach that enables useful analysis while protecting individually identifiable data values. You will also learn about the factors that influence the predictions models make.
Name:Monitor machine learning deployments
Description:Machine learning models can often encapsulate unintentional bias that results in unfairness. In this module, you will learn how to use Fairlearn and Azure Machine Learning to detect and mitigate unfairness in your models. You will learn how to use telemetry to understand how a machine learning model is being used once it has been deployed into production. Finally, you will learn how to monitor data drift to ensure your model continues to predict accurately.
Azure Machine Learning is a cloud platform for training, deploying, managing, and monitoring machine learning models. In this course, you will learn how to use the Azure Machine Learning Python SDK to create and manage enterprise-ready ML solutions. This is the third course in a five-course program that prepares you to take the DP-100: Designing and Implementing a Data Science Solution on Azurecertification exam. The certification exam is an opportunity to prove knowledge and expertise operate machine learning solutions at a cloud-scale using Azure Machine Learning. This specialization teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. Each course teaches you the concepts and skills that are measured by the exam. This Specialization is intended for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. It teaches data scientists how to create end-to-end solutions in Microsoft Azure. Students will learn how to manage Azure resources for machine learning; run experiments and train models; deploy and operationalize machine learning solutions, and implement responsible machine learning. They will also learn to use Azure Databricks to explore, prepare, and model data; and integrate Databricks machine learning processes with Azure Machine Learning.
