Azure Machine Learning service
GPTKB entity
Statements (68)
Predicate | Object |
---|---|
gptkbp:instance_of |
gptkb:Cloud_Computing_Service
|
gptkbp:bfsLayer |
5
|
gptkbp:bfsParent |
gptkb:Azure_ML_Workbench
|
gptkbp:developed_by |
gptkb:Microsoft
|
https://www.w3.org/2000/01/rdf-schema#label |
Azure Machine Learning service
|
gptkbp:integrates_with |
gptkb:Azure_Active_Directory
gptkb:Azure_Data_Lake_Storage gptkb:philosopher gptkb:Azure_Cognitive_Services gptkb:Azure_Blob_Storage gptkb:Azure_SQL_Database gptkb:Azure_Databricks gptkb:Power_BI gptkb:Azure_Dev_Ops gptkb:technology |
gptkbp:offers |
gptkb:software_framework
community support monitoring tools training resources user-friendly interface custom model training data preparation tools model deployment cost management tools visual interface model management batch inference deployment options experiment reproducibility model serving model interpretability tools pipeline orchestration |
gptkbp:provides |
hyperparameter tuning
resource management scalability security features user management machine learning capabilities model versioning data ingestion tools notebooks experiment tracking real-time inference experiment management data privacy features data labeling services data exploration tools scoring services AI model lifecycle management |
gptkbp:supports |
gptkb:lake
gptkb:fortification gptkb:Jupyter_Notebooks gptkb:R gptkb:Cloud_Computing_Service gptkb:Library collaborative development data governance data visualization multi-cloud environments data science model evaluation distributed training model optimization open-source frameworks edge deployment AI ethics. RESTAP Is M Lflow |