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Cognitecta

Training / Cloud & Platform

Intermediate · 2 days

AI Systems on Azure

A two-day course for cloud engineers, developers and architects: design enterprise AI systems on Azure, including identity, search, model APIs and the architecture those services should sit inside.

2 days · Intermediate · Cloud & Platform · Cloud engineers, developers and architects

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Overview

Azure is often chosen for enterprise AI because of identity, compliance and the Microsoft estate already in place. That is a reason to be more careful about architecture, not less. This course teaches how to design AI systems on Azure without treating a managed agent or a search resource as the whole design.

The course covers model APIs, Microsoft Entra integration, Azure AI services, RAG and search, storage, APIs, agents, security, observability, networking, deployment and cost. Identity is a thread through the two days: who the user is, what they may retrieve, and what a tool may do.

As with the AWS course, services are implementations. You design the system first, then map it onto current Azure AI, search and hosting capabilities.

You leave able to build an enterprise-shaped assistant or application on Azure with identity, retrieval and operations that would survive a security review.

Audience

Cloud engineers, developers and architects working on Azure who will design or implement enterprise AI systems.

Prerequisites

Working knowledge of Azure identity, resource organisation and a compute option. Familiarity with Entra ID and with LLM applications is helpful.

Duration

2 days

Can be aligned to your organisation’s Azure landing zone, Entra configuration and approved AI services.

Learning outcomes

  1. 01

    Design an Azure AI architecture with Entra identity, data boundaries and a model-access path.

  2. 02

    Implement model APIs and Azure AI services behind an application contract, not as the application itself.

  3. 03

    Build RAG using Azure storage and search with permissions that match enterprise identity.

  4. 04

    Integrate agents and tools with network, identity and API constraints.

  5. 05

    Apply security, observability, networking and cost controls appropriate to an enterprise estate.

  6. 06

    Deploy a working system and review it as a security and operations artefact.

Outline

  1. 01

    Enterprise shape on Azure

    • Architecture first: application, gateway, retrieval, tools, evaluation.
    • Model APIs and Azure AI services as implementations of inference.
    • Microsoft Entra: users, service principals, on-behalf-of, and assistant sessions.
    • Lab: authenticated model access with Entra-backed identity.
  2. 02

    Search, RAG and APIs

    • Storage, document intelligence and enterprise search.
    • RAG with identity-aware retrieval.
    • Application APIs, networking and private endpoints where they are required.
    • Lab: a permissions-aware retrieval path.
  3. 03

    Agents, security and operations

    • Agents and tool calling against internal APIs.
    • Security, observability, networking and deployment.
    • Cost, quotas and operational ownership.
    • Lab: complete a small enterprise assistant path and review it.

Practical work

You build an Azure-backed path: Entra-authenticated access, retrieval over a small corpus, and an application or agent that calls a constrained API. The review focuses on identity, network, cost and operations.

Takeaways

  • A working Azure implementation of a small enterprise AI path
  • Reference architectures mapped to current Azure AI and search services
  • Identity, network and cost checklists
  • A mapping from portable architecture to Azure resources

Delivery

Cognitecta delivers private corporate training, on-site or as remote live training. Courses can be run as published, or adapted to your organisation’s stack, domain and experience level.

Instructor

Nicholas Johnson, AI architect and software engineer. He has a degree in Artificial Intelligence and around twenty years of professional technology training, including hundreds of courses for engineering teams and large organisations. About.

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