Statements (72)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:cloud_services
|
| gptkbp:enables |
collaboration among teams
automated model training experiment tracking |
| gptkbp:facilitates |
model deployment
|
| https://www.w3.org/2000/01/rdf-schema#label |
AI Platform Pipelines
|
| gptkbp:integrates_with |
gptkb:Big_Query
gptkb:cloud_storage |
| gptkbp:is_accessible_by |
REST API
|
| gptkbp:is_available_in |
multiple regions
|
| gptkbp:is_available_on |
gptkb:Google_Cloud_Console
|
| gptkbp:is_compatible_with |
gptkb:Jupyter_Notebooks
|
| gptkbp:is_integrated_with |
gptkb:Cloud_Dataflow
gptkb:Cloud_AI_Platform gptkb:Cloud_Functions gptkb:Cloud_Pub/_Sub gptkb:Cloud_SQL gptkb:Cloud_Spanner gptkb:Cloud_Memorystore gptkb:Cloud_Run |
| gptkbp:is_optimized_for |
gptkb:Kubernetes
|
| gptkbp:is_part_of |
Google Cloud AI services
MLOps practices AI development lifecycle |
| gptkbp:is_scalable |
large datasets
|
| gptkbp:is_used_by |
data scientists
machine learning engineers |
| gptkbp:is_used_for |
data analysis
data ingestion model evaluation feature engineering model validation data exploration model optimization model monitoring data quality checks model lifecycle management model retraining model comparison model serving model deployment automation |
| gptkbp:offers |
visualization tools
versioning for models pre-built components |
| gptkbp:provides |
monitoring capabilities
security features end-to-end machine learning workflows resource management tools logging features pipeline orchestration API for custom integrations user interface for pipeline management |
| gptkbp:supports |
gptkb:XGBoost
gptkb:Tensor_Flow gptkb:Kubeflow gptkb:scikit-learn data preprocessing cloud-native applications custom components data transformation hyperparameter tuning multi-cloud environments data augmentation real-time predictions batch processing data pipelines data labeling data versioning Python SDK CI/ CD practices |
| gptkbp:bfsParent |
gptkb:Vertex_AI
|
| gptkbp:bfsLayer |
6
|