Overview

This tutorial demonstrates how to use Vertex AI SDK to build a custom container that uses the Custom Prediction Routine model server to serve a PyTorch model on Vertex AI Predictions.

Dataset

This tutorial uses R.A. Fisher's Iris dataset, a small dataset that is popular for trying out machine learning techniques. Each instance has four numerical features, which are different measurements of a flower, and a target label that marks it as one of three types of iris: Iris setosa, Iris versicolour, or Iris virginica.

This tutorial uses iris dataset.

Objective

The goal is to:

• Train a model that uses a flower's measurements as input to predict what type of iris it is.
• Save the model.
• Build a custom PyTorch serving container with custom preprocessing using the Custom Prediction Routine feature in the Vertex AI SDK.
• Test the built container locally.
• Deploy the entire solution on Vertex Pipelines
• Test the deployed model on Vertex Endpoint

Pipeline

This tutorial focuses more on deploying this model with Vertex AI than on the design of the model itself.

Costs

This tutorial uses billable components of Google Cloud:

• Vertex AI

Learn about Vertex AI pricing, and use the Pricing Calculator to generate a cost estimate based on your projected usage.