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Enterprise Scale AI Factory

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Welcome to the official Enterprise Scale AI Factory — an enterprise AI landing zone, established 2019, WAF-aligned, designed for Azure Public Cloud and compatible with Azure Government and Sovereign Cloud.

What is the AI Factory?

The Enterprise Scale AI Factory is a plug-and-play solution that automates the provisioning, deployment, and management of AI projects on Azure using a template-driven approach.

  • AI-ready landing zones with templates for DataOps, MLOps, and GenAIOps.
  • Automatically deploys 1–35 Azure services in a WAF-aligned application landing zone. It supports the full AI spectrum — generative AI, deep learning, machine learning, and traditional application development: AI Foundry, Azure Machine Learning, Databricks, AI Search, AKS, Logic Apps, Container Apps, Azure Functions, PostgreSQL, SQL Database, and more.
  • Add or remove services at any time via feature flags — all wired up with private networking, RBAC, and monitoring automatically is created or cleaned up when removing resource.
  • Supports both GitHub Actions and Azure DevOps as orchestrators.

Note

This project uses Azure CLI, Bicep, and GitHub Actions or Azure DevOps pipelines. Choose the supported entrypoint and authentication setup for your selected release.

Configure, create and update

  • Keep one project's complete variables.json with dev and stage_prod settings, including the three environment subscriptions. YAML and .env routes remain supported. See Configuration and API.
  • Create a scale set with ADO-create-new-aifactory-scaleset.sh or GHA-create-new-aifactory-scaleset.sh.
  • Update/run a project with ADO-update-aifactory-and-run-project.sh or GH-update-aifactory-and-run-project.sh (GHA-... is an alias).
  • Integrate a trusted ITSM/portal runner through the API layer: validate, prepare, approve, execute and track the exact job. Preparation alone does not deploy.

Use the complete variable reference for current JSON/YAML/GitHub names, defaults and conditional requirements.

Main Purpose

  1. Marry multiple best practices: Secure Enterprise Scale AI Landing Zones + Secure GenAIOps/MLOps templates — GenAIOps templates built on unsecured infrastructure are incompatible with private-endpoint-based infra, so the two are designed together here.
  2. Plug-and-play: Dynamically creates infra resources per team, including subnet/IP calculation, private networking, RBAC, and ACL permissions on the data lake — fully automated.
  3. Template-based project delivery: Project-based structure (cost control, privacy, scalability) with ready-made templates for DataLake, DataOps, MLOps, and GenAIOps.
  4. Enterprise scale, security, and battle-tested: Used by customers and partners with MLOps and GenAIOps since 2019.
  5. Intelligent CRUD for 30+ Azure resources: Enable and disable feature flags to add or safely remove resources. The AI Factory's internal dependency graph ensures that services are created, updated, and deleted in the correct order — including proper cleanup — without breaking dependent resources.
  6. Turn-key Enterprise Scale Data Lake with Datamesh: A structured, permission-layered data lake is provisioned automatically, with per-project ACL isolation and Datamesh-ready design, to marry data management with AI workloads.
  7. Flexible with 10+ BYO concepts: Bring Your Own IaC (Bicep, Terraform, ARM) on top of the AI Factory pipelines; BYO networking (VNet, subnets, routing tables); BYO Data Lake; BYO App Service Environment; BYO encryption key (CMK) — all configurable via a handful of parameters.

AI factory AI Application Landingzones: CONCEPTS & DESIGN: Differentiators?

  • The AI Factory wraps multiple environments together: Dev, Stage, Prod, per team, called AI Factory project.
  • The AI Factory sets up 1 to 3 AI Application Landing Zones per AI Factory project and project team (a team assigned to a project)
  • The AI Factory scales with Azure Subscriptions, called AI Factory scale sets, each team their own scaleset (3 Subscriptions = Application Landingzones)
  • Each AI Factory project (landingzone) is divided in two parts: COMMON & PROJECT specific, on resource group level - since required to align with WAF & CAF:
    • WAF Cost optimization: Reuse networking and common artifacts, across services in an architecture used by the use case and team
    • WAF Security: Least privileged access since the end-users does not need to have access on certain networking resources and other artifacts. Granular security
    • Operations: Reusing common artifacts also makes it easier to operate, such as Centralized Monitoring & Logging, Common security separated from granular Role Specific Access.
    • See full Documentation for more info
  • The AI Factory is designed, with its own compatible TEMPLATES for DataOps, MLOps, GenAIOps ( to avoid the challenges of incompatible security, etc)
  • Each AI Factory project can add or remove +34 Azure services - e.g. Not only the GenAI part of a solution, but also to avoid the challenges of incompatible security with ease and full automation of creating an End-2-End solution with 100% private networking for: - Full AI: Both GenAI (Foundry) and Machine Learning (Azure Machine Learning, Azure Databricks) - Front-End (UI, Caching) - Back-End (Databases) - Integration (LogicApps, APIM, Eventhubs) - Soveregnity: On-premises link via Azure Arc to Azure Machine Learning, Kubernetes, for Sovereign Cloud purposes.

Architectures

The AI Factory supports two baseline architectures:

Architecture Description
ESML Enterprise Scale Machine Learning — discriminative AI, MLOps, DataOps
GenAI-1 Enterprise Scale GenAI — AI Foundry, AI Search, agentic scenarios

GenAI-1 Baseline (minimum)

AI Foundry · AI Search · 2× Storage · Key Vault · Monitoring · Dashboards · Private Networking

Optional add-ons (enabled via feature flags, can be added at any time):

Category Services
AI Microsoft Foundry, Azure OpenAI (standalone), Azure Machine Learning, Azure Speech, Azure Vision, Bing Grounding, ...
Front-end / Hosting Azure Container Apps, Azure Web App / Function App, AKS (private + Arc-enabled)
Data & Databases Cosmos DB, MongoDB, Azure SQL, PostgreSQL Flexible, Azure Cache for Redis
Integration & ETL Azure Data Factory, Databricks, Event Hubs, Logic Apps, APIM AI Gateway, Microsoft Fabric / OneLake
On-oremises & Sovereign Cloud BYO Encryption Key, Azure Arc for on-prem execution on Kubernetes
s
AI Factory Architectures
Resource Link
Setup Guide Configuration and API, GitHub Actions, Azure DevOps
Update Guide How-to update AI Factory
CAF Documentation AI Factory in Cloud Adoption Framework
WAF AI Workload Well-Architected Framework — Enterprise Scale AI Factory

Public References