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Azure AI & ML: Optimize Language Models for AI Applications · LearnSpace
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Azure AI & ML: Optimize Language Models for AI Applications

Курс от Whizlabs
Средний≈ 8.3 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course is designed to provide a comprehensive foundation in Azure Machine Learning, equipping learners with the skills to deploy, manage, and optimize ML models efficiently. Participants will begin by exploring model deployment and consumption in Azure ML, understanding how to operationalize machine learning solutions in production environments. The course progresses to managing and evaluating models, covering key concepts such as performance monitoring, retraining strategies, and best practices for ensuring model accuracy. Learners will gain expertise in Azure AutoML workflows, from data preparation to model selection and evaluation, ensuring automated yet effective ML development. Additionally, the course covers key aspects of MLOps, enabling seamless integration with Azure services for scalable and secure machine learning operations. This course is structured into multiple modules, each featuring lessons and video lectures that provide theoretical insights and hands-on practice. Participants will engage with approximately 3:00–4:00 hours of instructional content, ensuring both conceptual understanding and practical application. To reinforce learning, graded and ungraded assignments are included within each module to test the ability of learners in real-world scenarios. Module 1: Azure AI Foundry: End-to-End Model Development & Optimization Module 2: Optimize model training with Azure Machine Learning By end of this course, you will be able to learn Understand the concepts of Azure AI Foundry, including its role in model optimization, fine-tuning, and retrieval-augmented generation (RAG) strategies. Learn how to explore and manage the Model Catalog and Collections within Azure AI Foundry and ML, and use compute resources effectively. Gain practical experience testing and manually evaluating prompts in the Azure AI Foundry portal playground, including tracking prompt variants. Discover how to create and configure search indexes in the Azure portal, using Azure AI Search for enhanced data retrieval and model deployment.

Навыки, которые вы освоите

Retrieval-Augmented GenerationModel EvaluationFine-tuningMicrosoft AzureModel OptimizationModel DeploymentModel TrainingContinuous MonitoringPrompt PatternsCloud DeploymentAI WorkflowsData PreprocessingData PipelinesPrompt EngineeringMLOps (Machine Learning Operations)Prompt Engineering Tools

Программа курса

2 модулей · 39 учебных материалов

01Azure AI Foundry: End-to-End Model Development & Optimization15 материалов

Optimize language models for AI applications

Welcome to the CourseЧтениеAzure AI Foundry: End-to-End Model Development & Optimization - OverviewЧтениеAzure AI Foundry: Overview and DemoВидеоRetrieval Augmented Generation (RAG) in Azure AI and ML: OverviewВидео

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Whizlabs Instructor

Преподаватель курса

Azure AI & ML: Optimize Language Models for AI Applications
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Обучение на Coursera

≈ 8.3 ч

2 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Бразильский португальский, Корейский, Немецкий, Испанский, Японский, Казахский, Венгерский

Часть программы вашего университета
Optimizing Models: Fine-Tuning, RAG and Application StrategiesВидео
Model Catalog and Collections [Azure AI Foundry and ML]-OverviewВидео
Model Catalog and Collections [Azure AI Foundry and ML]-ComputeВидео
Test a deployed language model in the playgroundВидео
How to manually evaluate prompts in Azure AI Foundry portal playgroundВидео
Define and track prompt variantsВидео
Quickstart: Create a search index in the Azure portal - Azure AI SearchВидео
Optimize language models for AI applications - Practice AssignmentЗадание
Azure AI Foundry: End-to-End Model Development & Optimization - Graded AssignmentЗадание
Meet & GreetОбсуждение
Microsoft Azure Advanced ML Training and AI Foundry – Interactive Expert DialogueDIALOGUE
02Optimize model training with Azure Machine Learning24 материалов

Preparing Code, Integrating ML Flow, and Tracking Models

Preparing Code, Integrating ML Flow, and Tracking Models - OverviewЧтениеPreparing code for production scenariosВидеоConvert a notebook to a scriptВидеоRun a script as a command jobВидеоUse parameters in a command jobВидеоExploring The use of MLflow For Tracking ModelsВидеоTrack Metrics with Machine Learning FlowВидеоIntegrating ML Flow in Model Training FlowВидеоViewing Metrics and Evaluating ModelsВидеоPreparing Code, Integrating MLflow, and Tracking Models - Practice AssignmentЗадание

Building Pipelines, Components, and Optimizing Models

Creating and Using components in Azure Machine LearningВидеоCreating Pipelines in Azure Machine LearningВидеоCreating a Custom PipelineВидеоComponents in Azure Machine LearningВидеоPrebuilt Pipelines (Automobile Price Prediction)ВидеоUnderstanding MetricsВидео
Load and Transform Data in Azure Machine Learning using notebooksВидео
Parameters and HyperparametersВидео
Key Hyperparameters in Machine LearningВидео
Exam TipsВидео
Building Pipelines, Components, and Optimizing Models - Practice AssignmentЗадание
Optimize model training with Azure Machine Learning - Graded AssignmentЗадание
Key TakeawaysЧтение
Conclusion, What's Next, Job Roles, and Best PracticesВидео