Statements (65)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:Cloud_Computing_Service
|
| gptkbp:bfsLayer |
4
|
| gptkbp:bfsParent |
gptkb:SAS_Viya
|
| gptkbp:allows |
data preparation
|
| gptkbp:developed_by |
gptkb:Microsoft
|
| gptkbp:enables |
model training
|
| gptkbp:facilitates |
model deployment
|
| https://www.w3.org/2000/01/rdf-schema#label |
Microsoft Azure ML
|
| gptkbp:includes |
ML algorithms
|
| gptkbp:integrates_with |
gptkb:Azure_Data_Lake
gptkb:Azure_Blob_Storage gptkb:Power_BI gptkb:archive gptkb:Azure_Dev_Ops Azure services |
| gptkbp:offers |
gptkb:software_framework
community support security features cloud-based solutions scalable infrastructure experiment tracking collaborative workspaces data labeling services data science competitions real-time inferencing data exploration tools pre-built models business intelligence integration custom model deployment training pipelines |
| gptkbp:provides |
API access
compliance certifications monitoring tools user-friendly interface visualization tools machine learning capabilities model versioning learning resources model management model deployment options enterprise-grade features scoring and evaluation tools model interpretability tools notebooks for experimentation AI model lifecycle management SD Ks for developers |
| gptkbp:supports |
gptkb:R
gptkb:Library gptkb:Jupyter_notebooks team collaboration data governance data ingestion data privacy data visualization hyperparameter tuning multi-cloud environments real-time analytics containerization AI ethics open-source frameworks data science workflows data connectors model retraining batch inferencing edge deployment |