GCP Vertex AI API

A GCP Vertex AI API is a GCP service API for GCP Vertex AI.

• See: Vertex AI SDK for Python‎‎.

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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.

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