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Microsoft Azure for AI and Machine Learning

Курс от Microsoft
Средний≈ 21.9 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course provides hands-on experience with Microsoft Azure's AI and ML services. You will learn to set up, manage, and troubleshoot Azure-based AI & ML workflows. The course covers the entire ML lifecycle in Azure, from data preparation to model deployment and monitoring. By the end of this course, you will be able to: 1. Configure and manage Azure resources for AI & ML projects. 2. Implement end-to-end ML pipelines using Azure services. 3. Deploy and monitor ML models in Azure production environments. 4. Troubleshoot common issues in Azure AI & ML workflows. To be successful in this course, you should have intermediate programming knowledge of Python, plus experience with AI & ML infrastructure, core AI & ML algorithms and techniques, and the design and implementation of intelligent troubleshooting agents. Familiarity with statistics is also recommended.

Навыки, которые вы освоите

Microsoft AzureModel DeploymentCI/CDModel TrainingArtificial Intelligence and Machine Learning (AI/ML)AI WorkflowsAI SecurityData PipelinesVersion ControlIdentity and Access ManagementCloud DeploymentMLOps (Machine Learning Operations)Data StorageContinuous Monitoring

Программа курса

5 модулей · 118 учебных материалов

01Azure AI & ML environment setup29 материалов

Welcome to the course

Introduction to the AI/ML engineering advanced professional certificate programВидеоIntroduction to Microsoft Azure for AI and Machine LearningВидеоWelcome to the Coursera CommunityЧтениеMicrosoft updatesЧтение

Учитесь у экспертов

Microsoft

Преподаватель курса

Microsoft Azure for AI and Machine Learning
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Обучение на Coursera

≈ 21.9 ч

5 модулей

Язык: Английский

Субтитры: Арабский, Французский, Узбекский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Вьетнамский, Нидерландский, Корейский, Немецкий, Пушту, Русский, Тайский, Индонезийский, Шведский, Турецкий, Азербайджанский, Испанский, Хинди, Японский, Казахский, Венгерский, Польский

Часть программы вашего университета
Practice activity: Setting up your environment in Microsoft AzureЧтение
Reflection: Setting up your environment in Microsoft AzureЗадание
Walkthrough: Setting up your environment in Microsoft Azure (Optional)Чтение
Practice activity: Creating your code repositoryЧтение
Reflection: Creating your code repositoryЗадание
Walkthrough: Creating your code repository Part 1 (Optional)Видео
Walkthrough: Creating your code repository Part 2 (Optional)Видео
Course syllabus: Microsoft Azure for AI and Machine LearningЧтение

Configuring Azure resources

Step-by-step guide to configuring resources for AI/ML projectsЧтениеPractice activity: Configuring resourcesЧтениеReflection: Configuring resourcesЗаданиеWalkthrough: Configuring resources (Optional)Видео

Setting up Azure Machine Learning workspaces

Setting up Azure Machine Learning workspacesВидеоExplanation of workspace setupЧтениеPractice activity: Implementing the best practices for workspace setupЧтениеReflection: Implementing the best practices for workspace setupЗаданиеWalkthrough: Implementing the best practices for workspace setup (Optional)Видео

Implementing data storage solutions

Introduction to data storage solutionsВидеоExplanation of storage solutionsЧтениеPractice activity: Implementing data storage solutionsЧтениеReflection: Implementing data storage solutionsЗаданиеWalkthrough: Implementing data storage solutions (Optional)ВидеоKnowledge check: Implementing data storage solutionsЗадание

Module summary: Azure AI & ML environment setup

Summary: Setting up an AI/ML Azure environmentЧтениеGraded quiz: Setting up an AI/ML Azure environmentЗадание
02Data preparation and model training in Azure21 материалов

Data ingestion pipelines

Data preparation and model training in AzureВидеоGuide to creating ingestion pipelinesЧтениеPractice activity: Creating an ingestion pipelineЧтениеReflection: Creating an ingestion pipelineЗаданиеWalkthrough: Creating an ingestion pipeline (Optional)ВидеоKnowledge check: Creating an ingestion pipelineЗадание

Data preprocessing

Data preprocessingВидеоExplanation of preprocessing techniquesЧтениеPractice activity: Implementing preprocessing techniquesЧтениеReflection: Implementing preprocessing techniquesЗаданиеWalkthrough: Implementing preprocessing techniques (Optional)Видео

Model training

Model trainingВидеоHow to train models using Azure Machine LearningВидеоKnowledge check: Model trainingЗадание

Monitoring and logging training processes

Monitoring and logging training processesВидеоExplanation of monitoring and loggingЧтениеPractice activity: LoggingЧтениеReflection: LoggingЗаданиеWalkthrough: Implementing logging in ML systems (Optional)Видео

Module summary: Data preparation and model training in Azure

Summary: Data preparation and model training in AzureЧтениеGraded quiz: Data preparation and model training in AzureЗадание
03Model deployment and management in Azure24 материалов

Model deployment

Model deploymentВидеоModel deployment industry standardsЧтениеPractice activity: Deploying trained models (Optional)ЧтениеReflection: Deploying trained models (Optional)ЗаданиеWalkthrough: Deploying trained models (Optional)ВидеоPractice activity: Using AKS (Optional)ЧтениеReflection: Using AKS (Optional)ЗаданиеWalkthrough: Using AKS (Optional)ВидеоPractice activity: Authenticating to Azure Machine LearningЧтениеReflection: Authenticating to Azure Machine LearningЗаданиеWalkthrough: Authenticating to Azure Machine Learning (Optional)Видео

Implementing CI/CD pipelines

Implementing CI/CD pipelinesВидеоExplanation of CI/CD pipelinesЧтениеKnowledge check: Implementing CI/CD pipelinesЗаданиеHow to implement CI/CD pipelines Чтение

Monitoring deployed models

Continuing deployment best practicesВидеоIntroduction and explanation of model managementЧтениеKnowledge check: Monitoring deployed modelsЗаданиеExplanation of monitoring techniquesЧтениеPractice activity: Monitoring deployed modelsЧтениеReflection: Monitoring deployed modelsЗадание

Module summary: Model deployment and management in Azure

Summary: Model deployment and management in AzureЧтениеGraded quiz: Model deployment and management in AzureЗадание
04Troubleshooting Azure AI/ML workflows26 материалов

Systematic troubleshooting approaches

Common issues and troubleshooting guideВидеоExplanation of common issues in model deploymentЧтениеGuide to troubleshooting approaches in model deploymentЧтениеKnowledge check: Troubleshooting techniquesЗаданиеPractice activity: Designing an intelligent troubleshooting agentЧтениеReflection: Designing an intelligent troubleshooting agentЗаданиеWalkthrough: Designing an intelligent troubleshooting agent (Optional)ВидеоPractice activity: Troubleshooting a sample pipelineЧтениеReflection: Troubleshooting a sample pipelineЗаданиеWalkthrough: Troubleshooting a sample pipeline (Optional)Видео

Diagnostic and monitoring tools

Explanation of diagnostic tools in machine learning pipelinesЧтениеPractice activity: Using diagnostic and monitoring toolsЧтениеReflection: Using diagnostic and monitoring toolsЗаданиеWalkthrough: Using diagnostic and monitoring tools (Optional)ВидеоKnowledge check: Diagnostic and monitoring toolsЗадание

Implementing automated alerts and remediation

Implementing automated alerts and remediationВидеоExplanation of automation tools in machine learning pipelinesЧтениеPractice activity: Implementing automated alerts and remediationЧтениеReflection: Implementing automated alerts and remediationЗаданиеWalkthrough: Implementing automated alerts and remediation (Optional)ВидеоUsing additional Azure automation tools, Part 1Видео

Module summary: Troubleshooting Azure AI/ML workflows

Summary: Troubleshooting Azure AI/ML workflowsВидеоExamples and best practices for troubleshooting workflows in Azure AI/MLЧтениеGraded quiz: Troubleshooting Azure AI/ML workflowsЗаданиеHear from an expert: Real-world applications of high-stakes use casesВидео
05Toward systems integration18 материалов

Real-world use cases of Azure issues

Real-world Azure deployment issues and remediationsЧтениеReal-world example libraryЧтениеDiscussion: Remediation strategiesЧтение

Unsecured environments and ramifications

Unsecured environments and ramificationsВидеоExplanation of unsecured environmentsЧтениеData security breach examplesЧтениеPractice activity: Analyzing a case study (essay assignment with AI feedback)Задание

Ideating potential issues and solutions

Ideating potential issues and solutionsВидеоPractice activity: Ideating potential issuesЗаданиеDiscussion: Ideating potential issuesЧтениеExplanation of solutionsЧтениеHear from an expert: Applying AI responsiblyВидео

Module summary: Toward systems integration

Summary: Toward system integrationВидеоGraded quiz: Toward system integrationЗадание

Course summary: Microsoft Azure for AI and Machine Learning

Course summaryВидеоPeer-reviewed assignment: Drafting the technical report (AI graded)ЗаданиеInteractive resource guide: Tools and platforms for further learningЧтениеCongratulations on completing the course!Видео
Walkthrough: Monitoring deployed models (Optional)Видео
Using additional Azure automation tools, Part 2Видео