Statements (67)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:cloud_services
|
| 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:Azure_Dev_Ops gptkb:Git_Hub Azure services |
| gptkbp:offers |
gptkb:machine_learning
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 SDKs for developers model deployment options enterprise-grade features scoring and evaluation tools model interpretability tools notebooks for experimentation AI model lifecycle management |
| gptkbp:supports |
gptkb:Python
gptkb:R 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 |
| gptkbp:bfsParent |
gptkb:RStudio_Server_Pro
gptkb:Google_Cloud_Auto_ML_API gptkb:SAS_Viya |
| gptkbp:bfsLayer |
5
|