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Catalogue / ml-platform

Azure Machine Learning

azureML platformazure-machine-learning

Workspace for training, tracking, registering and operationalizing machine learning models with MLOps.

Provider
azure
Kind
ML platform
Category
Compute
Official docs
https://learn.microsoft.com/en-us/azure/machine-learning/overview-what-is-azure-machine-learning
Also known as
Azure ML, AML
Properties · 3 set by this component
propertytypevaluesrequiredkind defaultthis component
automatedMl · component propertyboolean—notrueinherited
computeInstancesinteger—no—inherited
computeType · component propertyenumserverless | compute-instance | compute-cluster | kubernetesnoserverlessinherited
experimentTrackingboolean—notrue▸ true
gpuEnabledboolean—no—inherited
managedboolean—nofalse▸ true
managedNetworkIsolation · component propertyenumdisabled | allow-internet-outbound | allow-only-approved-outboundno—inherited
modelRegistryboolean—notrue▸ true
promptFlow · component propertyboolean—notrueinherited
publicAccessboolean—no—inherited
Rules that apply · 1

Every rule whose selector covers this component's kind or its category. Whether a rule fires depends on the design: its properties, its edges and its groups.

Stateful node without zone redundancyerror
stateful-node-without-zone-redundancy

A stateful compute node pinned to exactly one availability zone disappears with that zone, and its state goes with it. Nodes inside a cell are exempt, because there the cell is deliberately single-zone and redundancy comes from the other cells.

  • — all of these hold:
  • — the node's `stateless` is false
  • — the node sits inside exactly 1 availability-zone group
  • — this does not hold:
  • — the node sits inside at least one cell group
Use it in a design
nodes:
  - { id: "machine-learning", component: "azure-machine-learning" }
Other ML platform products · 61
Platform for AIalibaba-paialibabacloudml-platformAWS Clean Rooms MLaws-clean-rooms-mlawsml-platformAmazon SageMaker AIaws-sagemakerawsml-platformAmazon SageMaker Canvasaws-sagemaker-canvasawsml-platformAmazon SageMaker Feature Storeaws-sagemaker-feature-storeawsml-platformManaged MLflow on Amazon SageMaker AIaws-sagemaker-mlflowawsml-platformAmazon SageMaker Unified Studioaws-sagemaker-unified-studioawsml-platformMicrosoft Foundryazure-ai-foundryazureml-platformAbacus.AIabacus-aiml-platformAlteryxalteryxml-platformArgillaargillaml-platformC3 AI Platformc3-aiml-platformClearMLclearmlml-platformCometcomet-mlml-platformCVATcvatml-platformDataikudataikuml-platformDataRobotdatarobotml-platformDatasaurdatasaurml-platformDetermined AIdeterminedml-platformDomino Data Labdomino-data-labml-platformDVCdvcml-platformEncordencordml-platformFeastfeastml-platformFlyteflyteml-platformH2O.aih2oml-platformKilikiliml-platformKNIMEknimeml-platformKubeflowkubeflowml-platformLabel Studiolabel-studioml-platformLabelboxlabelboxml-platformMetaflowmetaflowml-platformGeneric ML platformml-platformml-platformMLflowmlflowml-platformneptune.aineptune-aiml-platformNVIDIA DGX Cloudnvidia-dgx-cloudml-platformPachydermpachydermml-platformPolyaxonpolyaxonml-platformProdigyprodigyml-platformRapidMiner AI Hubrapidminerml-platformRoboflowroboflowml-platform
and 21 more → ML platform page
Source
# yaml-language-server: $schema=../../schema/component.schema.json
# Azure Machine Learning workspace: the project container for data, jobs, registered models and compute used to train and operationalize models.
# "What is Azure Machine Learning?" states the workspace ships MLflow integration, job artifacts and lineage for tracking, a model catalog and registry, and serverless, cluster and Kubernetes compute; the docs do not state a default network isolation mode, so it is left absent.
schemaVersion: "1"
type: "component"
id: "azure-machine-learning"
kind: "ml-platform"
provider: "azure"
docs: "https://learn.microsoft.com/en-us/azure/machine-learning/overview-what-is-azure-machine-learning"
aliases: ["Azure ML", "AML"]
defaults:
  managed: true
  modelRegistry: true
  experimentTracking: true
properties:
  computeType:
    type: "enum"
    values: ["serverless", "compute-instance", "compute-cluster", "kubernetes"]
    default: "serverless"
  managedNetworkIsolation:
    type: "enum"
    values: ["disabled", "allow-internet-outbound", "allow-only-approved-outbound"]
  automatedMl:
    type: "boolean"
    default: true
  promptFlow:
    type: "boolean"
    default: true

The same page as markdown, for an agent: /catalogue/components/azure-machine-learning.md