Google Cloud AI Platform Pipelines Jobs
GPTKB entity
Statements (72)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:Cloud_Computing_Service
|
| gptkbp:bfsLayer |
3
|
| gptkbp:bfsParent |
gptkb:Job_Search_Engine
|
| gptkbp:allows |
versioning of models
|
| gptkbp:enables |
automated model training
reproducibility of experiments |
| gptkbp:facilitates |
collaboration among data scientists
|
| https://www.w3.org/2000/01/rdf-schema#label |
Google Cloud AI Platform Pipelines Jobs
|
| gptkbp:integrates_with |
gptkb:AI_Platform_Prediction
gptkb:Cloud_Dataflow gptkb:Big_Query gptkb:Cloud_Functions gptkb:Cloud_Pub/_Sub gptkb:Cloud_SQL gptkb:Graphics_Processing_Unit gptkb:Cloud_Spanner gptkb:Cloud_Computing_Service gptkb:Cloud_Memorystore gptkb:Cloud_Run Cloud IAM AI Platform Training |
| gptkbp:is_part_of |
Google Cloud AI ecosystem
|
| gptkbp:offers |
monitoring capabilities
user-friendly interface visualization tools pipeline templates cost management tools data preprocessing tools model serving capabilities data import/export tools collaborative tools for teams RESTAPI access training job management |
| gptkbp:provides |
user authentication
customizable dashboards job scheduling resource management security features automated testing machine learning workflows data source management logging features user interface for pipeline management API for pipeline execution scalability for ML workloads |
| gptkbp:supports |
gptkb:Kubeflow_Pipelines
team collaboration cloud-native applications custom components data governance data integration data lineage tracking data transformation data validation hyperparameter tuning model evaluation containerized applications data augmentation real-time predictions model deployment data exploration experiment tracking multi-cloud deployments model monitoring data orchestration data quality checks data science workflows Python SDK model retraining data pipeline automation data science best practices CI/ CD for ML models |