Statements (65)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:machine_learning
gptkb:AI_technology |
| gptkbp:developed_by |
gptkb:Google_Cloud
|
| gptkbp:enables |
Collaboration on ML Projects
|
| https://www.w3.org/2000/01/rdf-schema#label |
Vertex AI
|
| gptkbp:integrates_with |
gptkb:Kubernetes
gptkb:AI_Platform_Pipelines gptkb:Big_Query gptkb:Cloud_Functions gptkb:cloud_storage gptkb:Cloud_Run Third-party Tools |
| gptkbp:offers |
gptkb:App_Store
gptkb:Hyperparameter_Tuning Data Visualization Tools Cost Management Tools Model Deployment Notebooks Data Labeling Services Experiment Tracking Model Evaluation Tools Cloud-based Development Environment Support for Various Data Formats Pipeline Orchestration Custom Model Serving Data Ingestion Tools Explainable AI Tools Training on GPUs and TPUs |
| gptkbp:provides |
gptkb:Data_Science_Workbench
gptkb:AI_technology Resource Management Collaboration Features Model Monitoring Performance Optimization Tools Security Features Pre-trained Models Documentation and Tutorials User Management Features Training Infrastructure Access to Research Papers and Case Studies End-to-End ML Workflow Feedback Mechanisms for Users User Interface for ML Management |
| gptkbp:release_date |
gptkb:2021
|
| gptkbp:supports |
gptkb:Federated_Learning
gptkb:Auto_ML Community Contributions Cross-Platform Compatibility Multi-Cloud Deployment Data Privacy Compliance Data Preparation Model Retraining Scalable Training Integration with Jupyter Notebooks Batch Prediction Real-time Prediction Version Control for Models Custom Training Continuous Integration/ Continuous Deployment (CI/ CD) for ML |
| gptkbp:uses |
gptkb:Tensor_Flow
gptkb:Py_Torch |
| gptkbp:bfsParent |
gptkb:Google_AI
gptkb:Google_Cloud_AI gptkb:Google_Cloud_AI_Platform_Feature_Store |
| gptkbp:bfsLayer |
5
|