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Course Outline

Introduction to Microsoft Azure

  • Overview of Azure services and cloud computing paradigms
  • Establishing an Azure subscription and development environment
  • Understanding resource groups, virtual machines, and networking components

Building Event-Driven and Serverless Architectures

  • Introduction to Azure Functions and serverless computing models
  • Creating event-driven applications using Azure Event Grid and Service Bus
  • Developing serverless APIs and automated workflows

Managing Storage and Databases in Azure

  • Exploring Azure Storage options (Blob, Table, Queue, File)
  • Managing Azure SQL Database and Cosmos DB
  • Integrating storage solutions into cloud applications

Deploying Web Applications in Azure

  • Understanding Azure App Service and its deployment models
  • Building and deploying containerized applications using Docker
  • Scaling web applications using Kubernetes and Azure Container Instances

Integrating AI and Machine Learning in Cloud Apps

  • Introduction to Azure AI and Cognitive Services
  • Using Azure Machine Learning Studio to develop models
  • Implementing computer vision and natural language processing

DevOps and CI/CD in Azure

  • Setting up CI/CD pipelines using Azure DevOps
  • Managing infrastructure as code with Terraform and Bicep
  • Monitoring and logging applications using Azure Monitor

Enhancing Development with GitHub Copilot

  • Introduction to GitHub Copilot and AI-powered coding assistance
  • Using Copilot to write, debug, and optimize cloud application code
  • Best practices for leveraging AI-assisted coding in cloud development

Capstone Project: Building an AI-Powered Cloud Application

  • Designing a scalable AI cloud solution
  • Developing and deploying the application
  • Optimizing performance, security, and monitoring

Summary and Next Steps

Requirements

  • Foundational knowledge of cloud computing principles
  • Proficiency in at least one programming language (Python, JavaScript, or C# recommended)
  • Familiarity with web application development and database systems

Target Audience

  • Cloud developers and software engineers
  • AI practitioners and data scientists interested in integrating AI with cloud infrastructure
  • IT professionals and DevOps engineers
 35 Hours

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