This 2-days training provides a comprehensive introduction to deploying and managing artificial intelligence (AI) workloads on Microsoft Azure. It covers foundational elements such as the Microsoft Cloud Adoption Framework for Azure, Azure Landing Zones, and the overall...........
AI adoption process. Participants will gain essential knowledge on planning, implementing, and operating scalable and secure AI solutions in the cloud, with guidance on aligning AI strategies to organizational goals.
Key takeaways
• Understand the foundational components for deploying AI workloads on Azure
• Gain insights into the Microsoft Cloud Adoption Framework for structured cloud migration and adoption
• Learn how Azure Landing Zones support scalable and secure AI deployment
• Recognize the end-to-end AI adoption process, from strategy development to solution implementation
• Develop the ability to align AI initiatives with organizational objectives
Prerequisites
• Basic understanding of cloud computing concepts
• Familiarity with Microsoft Azure
• General knowledge of Artificial Intelligence and Machine Learning concepts
• Experience with IT infrastructure or application development (recommended)
Target audience
This training is designed for IT Professionals, Cloud Architects, Solution Architects, Data Scientists, and Technical Decision-Makers who are interested in adopting or managing AI solutions on Azure. It is also suitable for those responsible for cloud migration, operations, and the architecture of AI workloads within their organizations.
Module 1 – Microsoft Cloud Adoption Framework for Azure and Azure Landing Zone
Introduction to AI Workloads on Azure
This section introduces the concept of deploying AI workloads on Microsoft Azure. It provides an overview of the foundational elements required to successfully implement and manage AI solutions in the cloud.
Microsoft Cloud Adoption Framework for Azure
The Microsoft Cloud Adoption Framework for Azure offers structured guidance for migrating and adopting cloud technologies. Understanding this framework is essential for building a strong foundation for AI workloads on Azure.
Azure Landing Zone
The Azure Landing Zone serves as the starting point for deploying cloud resources. It ensures that all necessary components are in place to support scalable and secure AI workloads.
AI Adoption Process
Adopting AI involves a clear process, beginning with an understanding of AI capabilities and progressing through strategy development, planning, and building AI solutions on Azure.
• AI Strategy: Outlines the steps to develop a comprehensive AI strategy aligned with organizational goals.
• AI Plan: Details the process to plan for AI adoption, including resource allocation and timeline management.
• AI Ready: Describes the steps required to build and deploy AI workloads in Azure, ensuring readiness for production environments.
AI Platforms on Azure
• AI on Azure Platforms (PaaS): Discusses the use of Platform as a Service offering for AI deployment on Azure.
• AI on Azure Infrastructure (IaaS): Explores leveraging Infrastructure as a Service for running AI workloads.
Management, Governance, and Security
Managing, governing, and securing AI workloads are critical to maintaining compliance and operational integrity within Azure environments.
Landing Zone for Azure OpenAI
This section covers the specific considerations for establishing a landing zone tailored to Azure OpenAI services, ensuring AI solutions are built on a secure and scalable foundation.
Module 2 – Establish AI Operations
Generic AI Operations Overview
Introduces the foundational aspects of operating AI workloads on Azure, focusing on processes and best practices for effective management.
Design Areas for AI Workloads on Azure
Provides an overview of key design areas to consider when architecting AI solutions, ensuring optimal performance and reliability.
Generic AI Operations
Describes generic operational processes for managing AI workloads, including monitoring, maintenance, and scaling strategies on Azure.
Testing and Evaluation
Introduces methodologies for testing and evaluating AI workloads on Azure to ensure they meet performance and quality standards.
Workload Team Personas
Identifies and defines the roles and responsibilities of teams involved in managing and operating AI workloads on Azure.
Module 3 – Azure Well Architected Framework
AI Repeatable Patterns
Examines proven patterns that can be repeatedly used to design and deploy AI workloads on Azure efficiently.
Azure Well-Architected Framework
Explores the core principles and pillars of the Azure Well-Architected Framework, offering guidance for building robust AI solutions.
Design Methodology and Principles for AI Workloads
Outlines the methodology and principles for designing AI workloads, emphasizing best practices and architectural standards.
Design Areas for AI Workloads
Details the specific design considerations for building AI workloads on Azure, including scalability, reliability, and maintainability.
Testing and Evaluation
Describes the process for testing and evaluating AI workloads to ensure they are architected for success on Azure.
Responsible AI in Azure Workloads
Addresses the importance of responsible AI practices, ensuring ethical considerations are embedded in the development and deployment of AI solutions.
Well-Architected Framework AI Workload Assessment
Provides guidance for assessing AI workloads against the Azure Well-Architected Framework to ensure alignment with best practices and organizational goals.
Kurset inkluderer:
Course documentation and exercises. Lunch and refreshments for in class events only.