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Vertex AI SDK

As explained in Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

The Vertex AI SDK is a software development kit that provides programmatic access to Vertex AI services from supported applications.

The Vertex AI SDK provides programmatic access to Vertex AI services from Python, Java, and Node.js applications. In a practical deployment, it lets a Flask server send prediction requests to a Vertex AI endpoint instead of loading the model locally. section 10.5 section 10.5.1

The SDK supports several stages of a machine-learning workflow:

  • Install the Python package to access Vertex AI APIs. section 10.5.1
  • Invoke deployed models through Vertex AI endpoints. section 10.5
  • Identify managed datasets and prepare training, validation, and test subsets in Cloud Storage. section 11.2.7
  • Adapt training scripts for Vertex AI containers by reading Cloud Storage URIs, AIP environment variables, and the model output location. section 11.3.5

Install the Python SDK

A Python application can obtain the required API access by installing the package with `pip install google-cloud-aiplatform`, then using the SDK in the Flask deployment or pipeline workflow. section 10.5.1

pip install google-cloud-aiplatform

Use the installation listing as the starting point for configuring Python access to Vertex AI services. section 10.5.1

How Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI covers it

How deep it goes
Explains installation and concrete deployment or pipeline adaptations, but defers comprehensive SDK API details to external documentation.
Examples
Uses a Kuala Lumpur property-price predictor, a Flask endpoint client, CSV dataset splits, and container environment variables.
What it assumes
Assumes familiarity with Python applications, Flask, Cloud Storage, and machine-learning deployment concepts while teaching SDK installation from the command line.
What you can do after
Operate/configure Python applications and training pipelines that call Vertex AI endpoints and consume pipeline-managed data.
How it connects
Connects Python applications with Flask, Vertex AI endpoints, Cloud Storage, managed datasets, containers, and machine learning pipelines.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI. The section links point to the parts of the book it draws on.