Azure ML Workbench

GPTKB entity

Statements (71)
Predicate Object
gptkbp:instance_of gptkb:software_framework
gptkbp:bfsLayer 4
gptkbp:bfsParent gptkb:Microsoft_cloud_services
gptkbp:allows Experiment tracking
gptkbp:developed_by gptkb:Microsoft
gptkbp:enables Model evaluation
Automated machine learning
gptkbp:facilitates Model deployment
https://www.w3.org/2000/01/rdf-schema#label Azure ML Workbench
gptkbp:includes Visual interface
gptkbp:integrates_with Azure cloud services
gptkbp:is_available_on gptkb:operating_system
gptkbp:is_compatible_with gptkb:Azure_Dev_Ops
gptkbp:is_designed_for Data scientists
Enterprise solutions
Machine learning engineers
gptkbp:is_part_of gptkb:Azure_Machine_Learning_service
Azure ecosystem
AI development lifecycle
gptkbp:is_used_for Data-driven decision making
Anomaly detection
Fraud detection
Customer segmentation
Marketing analytics
Transportation logistics
Supply chain optimization
Social media analytics
Time series forecasting
Energy consumption forecasting
Telecommunications analytics
Building machine learning models
Manufacturing optimization
gptkbp:is_used_in Natural language processing
Computer vision
Predictive analytics
Financial modeling
Healthcare analytics
Retail analytics
gptkbp:offers Collaboration features
Scalability options
Experiment management
Model monitoring
Integration with Git Hub
gptkbp:provides Compute resources
Security features
Data visualization tools
Collaboration with stakeholders
Documentation and tutorials
Data preparation tools
Model training capabilities
Integration with Power BI
Access to Azure Marketplace
Integration with Azure Data Lake
Access to pre-built algorithms
Data labeling tools
gptkbp:supports gptkb:R
gptkb:Library
gptkb:Jupyter_notebooks
Data governance
Docker containers
Data transformation
Feature engineering
Hyperparameter tuning
Data ingestion
Multi-cloud deployment
Real-time inference
Interactive data exploration
Model retraining
Model versioning
Batch scoring
Custom model development