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.
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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.
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 Platforms on Azure
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.
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.
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.
Hvem passer dette kurset for?
Kurset er laget for IT-profesjonelle, Cloud Architects, Solution Architects, Data Scientists og tekniske beslutningstakere som ønsker å ta i bruk eller forvalte AI-løsninger på Microsoft Azure. Det passer også for de som har ansvar for migrasjon, drift og arkitektur av AI-workloads i egen organisasjon.
Hvilke forkunnskaper bør jeg ha?
Du bør ha:
Grunnleggende forståelse av skytjenester
Kjennskap til Microsoft Azure
Generell kunnskap om AI og maskinlæring
Erfaring med IT-infrastruktur eller applikasjonsutvikling er anbefalt
Hva lærer jeg i kurset?
Du får innsikt i å:
Planlegge og implementere AI-workloads i Azure
Bruke Microsoft Cloud Adoption Framework og Azure Landing Zones
Forstå hele AI-adopsjonsprosessen fra strategi til implementering
Drifte, sikre og overvåke AI-workloads i skyen
Bruke Azure Well-Architected Framework og repeatable patterns for AI-arkitektur
Implementere ansvarlig AI i løsningene dine
Hvordan foregår kurset?
Kurset varer i 2 dager og kombinerer teori, diskusjoner og praktiske eksempler. Du lærer gjennom moduler som dekker strategi, arkitektur, drift, sikkerhet og governance av AI-workloads i Azure.
Er dette kurset praktisk?
Ja – kurset gir praktisk innsikt i hvordan du etablerer og drifter AI-løsninger i Azure, med fokus på “best practices” og skalerbare arkitekturmønstre.
Kan jeg delta digitalt?
Ja, kurset tilbys både fysisk i klasserom og som live virtuelt klasserom.
Hva slags materiell får jeg?
Du får kursmateriell som dekker rammeverk, designprinsipper, operasjonelle prosesser og ansvarlig AI-praksis – samt tilgang til instruktørens veiledning underveis.
Hva koster kurset?
Ordinær pris er 18.000 kr.
Gir kurset sertifisering?
Nei, dette kurset er ikke knyttet til en spesifikk sertifisering, men det gir et solid grunnlag for videre sertifiseringsløp innen Azure eller AI-relaterte Microsoft-eksamener.
Kan jeg få kurset spesialtilpasset?
Ja – kurset kan spesialtilpasses. Det kan innebære å legge inn organisasjonens egne caser, justere faglig innhold eller fokusere på bestemte moduler som er mest relevante for ditt team.
Kan jeg bestille kurset for min organisasjon?
Ja – kurset kan bestilles som bedriftsinternt kurs. Glasspaper kan levere det enten onsite eller digitalt, tilpasset organisasjonens behov
Hva er neste steg etter dette kurset?
Etter dette kurset er naturlige neste steg:
Sertifiseringer som Azure AI Engineer Associate (AI-102) eller Azure Solutions Architect Expert
Videre spesialisering i Azure-tjenester for avansert AI og maskinlæring
