Azure Fundamentals with AI

Skill Goal:

Understand the fundamentals of cloud computing and how Microsoft Azure fits within the cloud ecosystem.

Skills Developed:

• Explain the concept of cloud computing and its business value

• Identify the different cloud service models (IaaS, PaaS, SaaS)

• Understand the Microsoft Azure ecosystem and global infrastructure

• Navigate the Azure Portal interface

• Understand Azure subscriptions, resource groups, and billing basics

Sprint Outcome:

Ability to explain cloud computing fundamentals and confidently navigate the Azure Portal.

Skill Goal:

Learn how to manage Azure resources and organize cloud infrastructure effectively.

Skills Developed:

• Create and manage Azure resource groups

• Understand Azure resource hierarchy and governance

• Monitor usage, quotas, and billing insights

• Manage resources within the Azure Portal

• Apply best practices for organizing cloud resources

Sprint Outcome:

Ability to manage Azure resources and organize cloud infrastructure efficiently.

Skill Goal:

Deploy and manage virtual machines to run workloads in the Azure cloud.

Skills Developed:

• Understand the role of Azure Virtual Machines

• Create VMs using Azure Marketplace images

• Configure VM compute and storage options

• Access and verify VM functionality

• Monitor and manage running virtual machines

Sprint Outcome:

Ability to deploy and manage cloud-based virtual machines for real workloads.

Skill Goal:

Understand Azure storage solutions and how data is stored in the cloud.

Skills Developed:

• Understand Azure storage architecture

• Configure Azure Storage accounts

• Store and manage data using Azure Blob Storage

• Share files using Azure File Storage

• Understand storage options for different workloads

Sprint Outcome:

Ability to configure and manage cloud storage solutions for applications and data.

Skill Goal:

Understand cloud database services and how Azure supports data management.

Skills Developed:

• Understand relational database concepts in Azure

• Create and configure Azure SQL Database

• Connect applications to cloud databases

• Understand scalability and availability in Azure databases

• Manage database access and connectivity

Sprint Outcome:

Ability to deploy and manage relational databases within Azure cloud environments.

Skill Goal:

Apply cloud security principles and manage user identities within Azure.

Skills Developed:

• Understand Azure security concepts and responsibilities

• Use Azure Active Directory (Azure AD) for identity management

• Configure authentication and access controls

• Apply identity and access management principles

• Understand compliance and security monitoring in Azure

Sprint Outcome:

Ability to implement security and identity management practices in Azure environments.

Skill Goal:

Develop a foundational understanding of Artificial Intelligence and Machine Learning concepts.

Skills Developed:

• Explain Artificial Intelligence and Machine Learning concepts

• Differentiate AI and ML approaches

• Understand supervised, unsupervised, and reinforcement learning

• Identify real-world AI use cases across industries

• Understand training, testing, and validation data concepts

Sprint Outcome:

Ability to explain core AI concepts and recognize how AI solutions operate.

Skill Goal:

Understand how Microsoft Azure provides infrastructure and services for AI solutions.

Skills Developed:

• Understand Azure AI services and capabilities

• Explore Azure AI platform components

• Identify AI solutions available within Azure

• Compare Azure AI with other AI platforms

• Understand the role of AI in cloud-native applications

Sprint Outcome:

Ability to recognize how Azure enables AI-driven solutions.

Skill Goal:

Understand how Azure infrastructure supports AI workloads and machine learning environments.

Skills Developed:

• Understand GPU-enabled Azure virtual machines

• Explore Azure Machine Learning Compute resources

• Understand Azure Kubernetes Service (AKS) for AI workloads

• Scale AI infrastructure using Azure cloud services

• Monitor and manage AI infrastructure performance

Sprint Outcome:

Ability to identify and manage infrastructure required for AI workloads in Azure.

Skill Goal:

Learn how Azure cloud services combine to support real-world AI solutions.

Skills Developed:

• Combine storage, compute, and networking for AI workloads

• Understand architecture of cloud-based AI solutions

• Implement security best practices for AI systems

• Monitor AI workloads using Azure tools

• Analyze real-world Azure AI use cases

Sprint Outcome:

Ability to understand and design cloud infrastructure that supports AI applications.

Project Goal:

Design and analyze a cloud architecture that supports an AI-powered application using Microsoft Azure services.

Skills Demonstrated:

• Analyze a real-world AI application scenario

• Identify required Azure cloud services for the solution

• Design Azure infrastructure including VMs, storage, and networking

• Configure cloud resources to support AI workloads

• Evaluate AI infrastructure options such as GPU VMs and AKS

• Apply security and identity management principles

• Understand Azure Machine Learning compute environments

• Demonstrate how Azure services support scalable AI solutions

• Present a cloud architecture plan for an AI-based system

• Explain deployment considerations for AI applications in Azure