II.
LibraryProcess overview
Reference · livelib-process:data-science-ml--feature-store
feature-store overview
Feature Store Implementation and Management - Design, implement, and operationalize a feature store for ML feature management with quality gates, validation, serving consistency, and iterative refinement.
Attributes
displayName
feature-store
description
Feature Store Implementation and Management - Design, implement, and operationalize a feature store
for ML feature management with quality gates, validation, serving consistency, and iterative refinement.
libraryPath
library/specializations/data-science-ml/feature-store.js
specialization
data-science-ml
references
- - Feast Feature Store: https://docs.feast.dev/ - Feature Store for ML by Google: https://cloud.google.com/architecture/ml-feature-stores-best-practices - Tecton Feature Platform: https://www.tecton.ai/ - AWS SageMaker Feature Store: https://aws.amazon.com/sagemaker/feature-store/ - Hopsworks Feature Store: https://www.hopsworks.ai/
example
const result = await orchestrate('specializations/data-science-ml/feature-store', {
projectName: 'Recommendation System',
featureStoreType: 'online-offline',
dataCharacteristics: { featureCount: 150, updateFrequency: 'realtime', dataVolume: '10TB' },
servingRequirements: { latencyMs: 50, throughputQPS: 10000, consistency: 'eventual' }
});
usesAgents
- general-purpose
Outgoing edges
lib_applies_to_domain1
- domain:data-science·DomainData Science
lib_belongs_to_specialization1
- specialization:data-science-ml·Specialization
lib_implements_workflow1
- workflow:data-pipeline-deployment·WorkflowData Pipeline Deployment
lib_involves_role1
- role:data-scientist·RoleData Scientist
Incoming edges
None.