https://pgabriel-01.github.io/mlops-v3/planning/index.htmlPlan your deployment by working through the decisions below. Note This phase is optional. If you already know what you want, skip to Phase 1. Terms Accelerator module: the MLOpsAccelerator PowerShell module (Deploy-MLOpsAccelerator). Inputs file: the one inputs.yaml that drives everything. Scenario: one of 11 Azure ML network, serving and governance patterns. Bootstrap: the Terraform that creates the repository, environments, identity and Azure access before any ML infrastructure exists. Generated project: the repository the accelerator writes: infrastructure, CI/CD and ML workload. Environment: a full Azure ML workspace and its dependencies, one per stage (dev, test, prod). Skills that help Terraform or Bicep workflow: Terraform fundamentals, Bicep learning path Azure Machine Learning: Azure ML learning path CI/CD and git: Introduction to DevOps, Introduction to git Decisions Each decision maps to a key in inputs.yaml. The parameter checklist lists them all in one place, and the decision flow shows how the choices connect.Hugoen-ushttps://pgabriel-01.github.io/mlops-v3/planning/decision-flow/index.htmlMon, 01 Jan 0001 00:00:00 +0000https://pgabriel-01.github.io/mlops-v3/planning/decision-flow/index.htmlUse these flows to reach the values for inputs.yaml. The numbers refer to the planning decisions. Overall flowchart TD A([Start]) --> B{"Decision 1-2<br/>IaC and CI/CD"} B --> C["iac: terraform or bicep<br/>cicd: github or azdo"] C --> D{"Decision 3<br/>Scenario"} D --> E{"Decision 10<br/>Bootstrap networking"} E --> F["Decisions 4-9, 11-13<br/>region, subscription, environments,<br/>data, workloads, repository"] F --> G["New-MLOpsInputFile"] G --> H["Deploy-MLOpsAccelerator"] Choose a scenario (decision 3) flowchart TD S([Pick a scenario]) --> N{"Must traffic stay<br/>off the public internet?"} N -- No --> P{"What do you need?"} P -- Quick trial --> S1["1 basic-public"] P -- Batch scoring --> S5["5 batch-scoring"] P -- "Safe rollout of online endpoints" --> S6["6 online-bluegreen"] P -- Feature store --> S7["7 feature-store"] P -- "Promote models across environments" --> S8["8 registry-promotion"] P -- Budgets and policy --> S9["9 governance"] P -- "Generative AI" --> S10["10 genai-foundry"] N -- Yes --> V{"Do you have your own VNet<br/>or a platform landing zone?"} V -- "Platform landing zone" --> S11["11 existing-platform"] V -- "Own VNet" --> S4["4 byo-vnet"] V -- "No, let AML manage it" --> O{"Outbound access"} O -- "Open to the internet" --> S2["2 managed-vnet-internet"] O -- "Approved destinations only" --> S3["3 managed-vnet-approved"] See the scenarios page for cost and prerequisites of each.https://pgabriel-01.github.io/mlops-v3/planning/parameter-checklist/index.htmlMon, 01 Jan 0001 00:00:00 +0000https://pgabriel-01.github.io/mlops-v3/planning/parameter-checklist/index.htmlWork through this before running Deploy-MLOpsAccelerator. Always project_name (3 to 10 lowercase alphanumerics) location, subscription_id, tenant_id scenario (see scenarios) iac: terraform or bicep cicd: github (github_org, github_repo) or azdo (azdo_org_url, azdo_project) environments: default dev, test, prod; any ordered subset of those (promotion and approvals follow the order) tags Bootstrap networking use_private_networking (GitHub only): true if policy blocks public state storage; then review runners_vnet_address_space runner_label: ubuntu-latest, or the label of your self-hosted runners Workloads workloads: optional list; omit for one workload at the repository root By scenario 3: allowed_outbound_fqdns 4: vnet_address_space 9 and 11: budget_amount, alert_emails 11: existing_platform.subnet_id, .log_analytics_workspace_id, .private_dns_zone_ids (blob, file, vault, acr, aml_api, aml_notebooks) Prerequisites See Phase 1 - Prerequisites: tools, Azure permissions and resource providers, GitHub or Azure DevOps token, quota, and self-hosted agents for private scenarios. Test-MLOpsAcceleratorRequirement checks what it can.https://pgabriel-01.github.io/mlops-v3/planning/data-sources/index.htmlMon, 01 Jan 0001 00:00:00 +0000https://pgabriel-01.github.io/mlops-v3/planning/data-sources/index.htmlAML Import Data (preview) and Data Connections (preview) were retired on 2026-09-30, so the accelerator does not use them. Microsoft’s replacement is Fabric plus an AML datastore (migration guide). Design rule: AML only consumes a datastore or data asset. Getting data from Snowflake or Azure SQL into storage is a separate, optional step. Options tested “Tested” means validated offline on 2026-10-13 (no Azure resources were created and no Snowflake account was available). Items marked docs only rely on Microsoft documentation.