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Managed MLflow on Amazon SageMaker AI

awsML platformaws-sagemaker-mlflow

Managed MLflow tracking server for experiments, runs and model versions.

Provider
aws
Kind
ML platform
Category
Compute
Official docs
https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow.html
Properties · 3 set by this component
propertytypevaluesrequiredkind defaultthis component
artifactStore · component propertyenums3nos3inherited
automaticModelRegistration · component propertyboolean—no—inherited
computeInstancesinteger—no—inherited
experimentTrackingboolean—notrue▸ true
gpuEnabledboolean—no—inherited
managedboolean—nofalse▸ true
modelRegistryboolean—notrue▸ true
publicAccessboolean—no—inherited
trackingServerSize · component propertyenumsmall | medium | largenosmallinherited
zoneRedundant · component propertyboolean—nofalseinherited
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: "sagemaker-mlflow", component: "aws-sagemaker-mlflow" }
Other ML platform products · 61
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and 21 more → ML platform page
Source
# yaml-language-server: $schema=../../schema/component.schema.json
# Fully managed MLflow on Amazon SageMaker AI: an MLflow Tracking Server that records experiments,
# runs and model versions. "Accelerate generative AI development using managed MLflow on Amazon
# SageMaker AI" says the compute and backend metadata store are hosted in the SageMaker service
# account while the artifact store is an Amazon S3 bucket in your account, that the default tracking
# server size is Small, and that tracking servers launch in a single Availability Zone in their Region.
schemaVersion: "1"
type: "component"
id: "aws-sagemaker-mlflow"
kind: "ml-platform"
provider: "aws"
docs: "https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow.html"
defaults:
  managed: true
  experimentTracking: true
  modelRegistry: true
properties:
  trackingServerSize:
    type: "enum"
    values: ["small", "medium", "large"]
    default: "small"
  zoneRedundant:
    type: "boolean"
    default: false
  artifactStore:
    type: "enum"
    values: ["s3"]
    default: "s3"
  automaticModelRegistration:
    type: "boolean"

The same page as markdown, for an agent: /catalogue/components/aws-sagemaker-mlflow.md