Advanced MS Azure with AI

Skill Goal:

Develop the ability to configure and manage Azure environments used for enterprise cloud solutions.

Skills Developed:

• Understand Azure cloud architecture and service categories

• Use Azure Resource Manager (ARM) and resource groups

• Organize cloud workloads and resources efficiently

• Prepare Azure environments for development and deployment

• Navigate Azure portal and management tools

Sprint Outcome:

Ability to configure and manage an Azure environment that supports scalable cloud solutions.

Skill Goal:

Learn how to deploy and manage the core infrastructure services required for cloud applications.

Skills Developed:

• Deploy Azure Virtual Machines and App Services

• Configure Azure Functions for serverless workloads

• Manage Azure Storage and data services

• Implement Azure networking components

• Select the appropriate Azure services for different workloads

Sprint Outcome:

Ability to deploy and manage cloud infrastructure using Azure compute, storage, and networking services.

Skill Goal:

Understand how artificial intelligence is applied in modern business environments.

Skills Developed:

• Explain core AI and machine learning concepts

• Identify real-world AI applications across industries

• Recognize common AI solution architectures

• Understand responsible AI and ethical considerations

• Evaluate business scenarios where AI adds value

Sprint Outcome:

Ability to identify opportunities for applying AI technologies to business problems.

Skill Goal:

Apply Azure AI services to add intelligent capabilities to applications.

Skills Developed:

• Understand Azure Cognitive Services capabilities

• Apply Computer Vision and image recognition services

• Use speech recognition and synthesis services

• Apply natural language processing tools

• Work with Azure AI decision services

Sprint Outcome:

Ability to integrate AI capabilities such as vision, speech, and language into cloud applications.

Skill Goal:

Build data pipelines and manage datasets required for AI and analytics solutions.

Skills Developed:

• Build ETL workflows using Azure Data Factory

• Integrate multiple data sources for analytics

• Manage datasets using Azure SQL Database and Cosmos DB

• Prepare data for machine learning models

• Apply data transformation and preparation techniques

Sprint Outcome:

Ability to manage enterprise data pipelines and prepare datasets for AI applications.

Skill Goal:

Develop and deploy machine learning models using Azure Machine Learning services.

Skills Developed:

• Train machine learning models using Azure ML Studio

• Evaluate model performance and accuracy

• Apply model tuning techniques

• Deploy machine learning models to production environments

• Understand MLOps and model lifecycle management

Sprint Outcome:

Ability to build, evaluate, and deploy machine learning models within Azure cloud environments.

Skill Goal:

Create intelligent cloud applications that combine AI services with application logic.

Skills Developed:

• Integrate AI APIs into application workflows

• Design architecture for intelligent cloud applications

• Apply AI features in real application scenarios

• Develop AI-enabled cloud solutions

• Evaluate performance of intelligent applications

Sprint Outcome:

Ability to develop applications that integrate Azure AI services and cloud infrastructure.

Skill Goal:

Implement security and governance practices across Azure environments.

Skills Developed:

• Apply Azure security best practices

• Manage identity and access using Azure Active Directory

• Implement access control policies

• Understand governance and compliance requirements

• Protect cloud applications and data resources

Sprint Outcome:

Ability to design secure cloud environments that protect applications, users, and data.

Skill Goal:

Implement DevOps, CI/CD, and operational best practices in Azure environments.

Skills Developed:

• Understand DevOps workflows in Azure projects

• Implement CI/CD pipelines for cloud applications

• Optimize application performance in Azure environments

• Monitor cloud services and applications

• Maintain and update deployed applications

Sprint Outcome:

Ability to deploy and manage Azure applications using modern DevOps and cloud operations practices.

Project Goal:

Design and implement an intelligent cloud solution using Microsoft Azure services by integrating cloud infrastructure, data pipelines, machine learning models, and AI services to solve a real-world business problem.

Skills Demonstrated:

• Analyze a real-world business scenario requiring cloud and AI capabilities

• Design a scalable Azure architecture for the proposed solution

• Configure Azure compute, storage, and networking services

• Prepare and manage datasets using Azure data services

• Build and train a machine learning model using Azure Machine Learning

• Integrate Azure AI services such as vision, language, or decision APIs

• Deploy an intelligent cloud application using Azure services

• Apply security, identity, and governance best practices

• Monitor performance and optimize the deployed solution

• Present the architecture, insights, and business value to stakeholders