Introduction to Vertex AI Batch Prediction using Node.js

Vertex AI Batch Prediction allows you to perform batch prediction on large datasets using machine learning models. In this article, we will explore how to use Node.js to interact with Vertex AI Batch Prediction and run predictions on your data.

Setting up the Environment

Before you can start using Vertex AI Batch Prediction with Node.js, you need to set up your environment. The first step is to install the necessary libraries and packages using npm.


npm install @google-cloud/vertex-ai


Authenticating with Google Cloud

Next, you need to authenticate with Google Cloud in order to access Vertex AI Batch Prediction. You can do this by setting up a service account key and specifying the path to the key file as an environment variable.


export GOOGLE_APPLICATION_CREDENTIALS="path/to/service-account-key.json"


Using Vertex AI Batch Prediction with Node.js

Once your environment is set up and you are authenticated with Google Cloud, you can start using Vertex AI Batch Prediction in your Node.js application. First, you need to create a client for Vertex AI Batch Prediction using the @google-cloud/vertex-ai library.


const { VertexAI } = require('@google-cloud/vertex-ai');
const vertexAI = new VertexAI();


Once you have created a client, you can use it to run batch predictions on your machine learning model. You will need to specify the location of your model, the input data, and the output location for the predictions.


const modelLocation = 'projects/project-id/locations/us-central1/models/model-id';
const inputData = 'gs://bucket/input.csv';
const outputLocation = 'gs://bucket/output.csv';

const prediction = await vertexAI.batchPredict({
modelLocation,
inputData,
outputLocation
});


Conclusion

Using Node.js to interact with Vertex AI Batch Prediction allows you to easily run batch predictions on your machine learning models. By following the steps in this article, you can integrate Vertex AI Batch Prediction into your Node.js applications and make use of its powerful prediction capabilities.