GCP Vertex AI API
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A GCP Vertex AI API is a GCP service API for GCP Vertex AI.
References
2023
- GBard
- Vertex AI provides a rich set of APIs for managing your machine learning (ML) projects and resources. The following is a list of the most commonly used Vertex AI APIs:
- Vertex AI AutoML APIs: These APIs allow you to train and deploy ML models without writing any code.
- Vertex AI Custom Model APIs: These APIs allow you to train and deploy custom ML models using your own code.
- Vertex AI Model Serving APIs: These APIs allow you to deploy and serve ML models to production.
- Vertex AI Model Management APIs: These APIs allow you to manage the lifecycle of your ML models, including creating, storing, updating, and deleting models.
- Vertex AI Feature Store APIs: These APIs allow you to manage your ML features, including creating, storing, and retrieving features.
- Vertex AI Pipeline APIs: These APIs allow you to create and manage ML pipelines, which are workflows that automate the ML development process.
- Vertex AI TensorBoard APIs: These APIs allow you to visualize and monitor your ML training runs.
- Vertex AI Vizier APIs: These APIs allow you to optimize the hyperparameters of your ML models.
- Vertex AI Explainable AI APIs: These APIs allow you to explain the predictions of your ML models.
- Vertex AI ML Metadata APIs: These APIs allow you to manage the metadata of your ML resources, such as models, datasets, and pipelines.
- reference:
https://cloud.google.com/vertex-ai/docs/reference/rest
.
- Vertex AI provides a rich set of APIs for managing your machine learning (ML) projects and resources. The following is a list of the most commonly used Vertex AI APIs: