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MLOps and responsible AI practices · LearnSpace
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MLOps and responsible AI practices

Курс от Microsoft
Средний≈ 22.6 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course equips you with the essential skills to take generative AI models from development to production. You will learn to implement robust MLOps practices on Azure, including automated CI/CD pipelines, version control, and full lifecycle management for your models. Simultaneously, you will dive into the critical principles of Responsible AI, using Microsoft’s framework to build fair, transparent, and ethical models that you can deploy with confidence.

Навыки, которые вы освоите

MLOps (Machine Learning Operations)CI/CDResponsible AIAzure DevOpsVersion ControlGit (Version Control System)Azure DevOps PipelinesMicrosoft AzureData EthicsAI WorkflowsContinuous IntegrationGenerative AIModel TrainingModel DeploymentContinuous MonitoringModel Evaluation

Программа курса

4 модулей · 72 учебных материалов

01Introduction to MLOps and model lifecycle management20 материалов

Fundamentals of MLOps

Introduction to Microsoft GenAI engineering certificationВидеоIntroduction to MLOps in Azure AI EngineeringВидеоCourse syllabus and recommended backgroundЧтениеWhat is MLOps?Видео

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Microsoft

Преподаватель курса

MLOps and responsible AI practices
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Обучение на Coursera

≈ 22.6 ч

4 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Бразильский португальский, Корейский, Немецкий, Пушту, Испанский, Дари, Японский

Часть программы вашего университета
Principles of MLOps and the Azure toolkitЧтение
A guided tour of the MLOps toolkit in Azure MLВидео
Setting up MLOps in Azure MLЗадание
MLOps key takeawaysЧтение
MLOps basics: Practice QuizЗадание

Managing model lifecycles

The importance of gathering requirementsВидеоA practical guide to the model lifecycle in AzureЧтениеVisualizing the end-to-end model lifecycleВидеоManually managing a model in the Azure ML Model RegistryЗаданиеLifecycle management highlightsЧтениеManually registering and versioning a modelЗаданиеLifecycle management skills: Practice QuizЗаданиеMaking business-driven lifecycle decisionsЧтениеBeyond the workflow: Making smart lifecycle decisionsDIALOGUEModule 1 evaluation: Graded QuizЗаданиеModule 1 summary: From manual workflows to strategic managementВидео
02Version control and CI/CD pipelines16 материалов

Implementing version control

Module 2 introduction: From code commits to automated deploymentsВидеоImportance of version control in AIВидеоImplementing version control with Azure ReposЧтениеConnecting Azure Repos and Azure ML: A step-by-step guideВидеоImplementing version control with GitHub and Azure MLЗаданиеVersion control strategiesЧтениеVersion control proficiency: Practice QuizЗадание

CI/CD for AI models

Designing and implementing CI/CD pipelinesЧтениеCI/CD workflows in AzureВидеоImplementing an end-to-end CI/CD pipeline for AI modelsЗаданиеCI/CD techniquesЧтениеCI/CD workflow understanding: Practice QuizЗаданиеYour CI/CD pipeline as a quality gatekeeperDIALOGUE
03Monitoring, logging, and cost optimization18 материалов

Monitoring and logging practices

Module 3 introduction: From deployment to operational excellenceВидеоThe role of monitoring in AIВидеоSetting up logging and monitoring frameworksЧтениеA tour of Azure's monitoring and logging toolsВидеоConfiguring Azure monitoring toolsЗаданиеMonitoring best practicesЧтениеImplementing custom logging for an inference endpointЗаданиеFrom logs to insights: Analyzing custom logging dataЧтениеMonitoring and logging: Practice QuizЗадание

Cost optimization strategies

Managing costs with Azure ML compute and OpenAI servicesЧтениеOptimizing AI-related costs in AzureВидеоManaging and optimizing AI deployment costsЗаданиеStrategic cost management and trade-offsЧтениеCost management assessment: Practice QuizЗаданиеBeyond the calculator: the art of balancing cost and performanceDIALOGUE
04Ethical AI and Microsoft’s responsible AI practices18 материалов

Ethical AI in practice

Module 4 introduction: From a working model to a trustworthy systemВидеоWhy ethics matter in AIВидеоGuidelines for ethical AIЧтениеIntroducing the Azure Responsible AI DashboardВидеоBuilding an ethical AI checklistЗаданиеImplementing ethics in AIЧтениеEthical considerations in AI: Practice QuizЗадание

Microsoft’s responsible AI framework

Integrating Microsoft's responsible AI practices and AETHER guidelinesЧтениеImplementing responsible AI with Microsoft guidelinesВидеоImplementing responsible AI from assessment to mitigationЗаданиеResponsible AI implementationЧтениеResponsible and ethical AI analysis: Practice QuizЗаданиеIntegrating Responsible AI into your MLOps pipelineЧтение
Case study: Anatomy of a production-grade AI pipelineЧтение
Module 2 evaluation: Graded QuizЗадание
Module 2 summary: From automated deployment to production realityВидео
Achieving operational excellence: A unified approachЧтение
Module 3 evaluation: Graded QuizЗадание
Module 3 summary: From deployment to operational excellenceВидео
Hands-on final projectЗадание
Final Project rationale and strategy assessment: Graded projectЗадание
Module 4 summary: From ethical principles to an integrated pipelineВидео
MLOps and Responsible AI: Graded QuizЗадание
Course Summary: Integrating MLOps and ethics for production AIВидео