Google Cloud AI Platform Training Jobs
GPTKB entity
Statements (62)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:training
|
| gptkbp:can |
Using Cloud Functions
Using Cloud Scheduler |
| gptkbp:can_be_used_for |
Model evaluation
Model training Model deployment |
| https://www.w3.org/2000/01/rdf-schema#label |
Google Cloud AI Platform Training Jobs
|
| gptkbp:integrates_with |
gptkb:Big_Query
gptkb:cloud_storage |
| gptkbp:is_accessible_by |
REST API
Web UI gcloud command-line tool |
| gptkbp:is_available_in |
Multiple regions
|
| gptkbp:is_integrated_with |
Third-party tools
|
| gptkbp:is_managed_by |
gptkb:Google_Cloud
|
| gptkbp:is_optimized_for |
gptkb:performance
Cost efficiency |
| gptkbp:is_part_of |
gptkb:Google_Cloud_AI_Platform
Google Cloud ecosystem |
| gptkbp:is_scalable |
Large datasets
Complex models |
| gptkbp:is_used_by |
gptkb:researchers
Data scientists Machine learning engineers |
| gptkbp:offers |
Hyperparameter tuning
Distributed training Experiment tracking Cost management tools Model versioning Training metrics Custom training jobs Pre-built containers Preprocessing capabilities |
| gptkbp:provides |
Scalability
Resource management User-friendly interface Security features Job monitoring Job prioritization Logging and debugging tools Managed training services |
| gptkbp:requires |
Google Cloud account
|
| gptkbp:supports |
gptkb:Tensor_Flow
gptkb:Auto_ML gptkb:Py_Torch gptkb:Scikit-learn gptkb:Jupyter_notebooks Data versioning IAM roles Data parallelism Real-time inference Service accounts Model parallelism Batch prediction Online prediction GPU training Custom training loops Multi-node training Custom Docker images TPU training |
| gptkbp:bfsParent |
gptkb:Google
|
| gptkbp:bfsLayer |
4
|