Getting predictions beginner's guide

This beginner's guide is an introduction to getting predictions from custom models on Vertex AI.

Learning Objectives

Vertex AI experience level: Beginner

Estimated reading time: 15 minutes

What you will learn:

  • Benefits of using a managed prediction service.
  • How batch predictions work in Vertex AI.
  • How online predictions work in Vertex AI.

Why use a managed prediction service?

Imagine you've been tasked with creating a model that takes as input an image of a plant, and predicts the species. You might start by training a model in a notebook, trying out different hyperparameters and architectures. When you have a trained model, you can call the predict method in your ML framework of choice and test the model quality.

This workflow is great for experimentation, but when you want to use the model to get predictions on lots of data, or get low latency predictions on the fly, you're going to need something more than a notebook. For example, let's say you're trying to measure the biodiversity of a particular ecosystem and instead of having humans manually identify and count plant species out in the wild, you want to use this ML model to classify large batches of images. If you're using a notebook, you might hit memory contstraints. Additionally, getting predictions for all of that data is likely to be a long running job that might timeout in your notebook.

Or what if you wanted to use this model in an application where users could upload images of plants and have them identified immediately? You'll need some place to host the model that exists outside of a notebook that your application can call to for a prediction. Additionally, it's unlikely you'll have consistent traffic to your model, so you'll want a service that can autoscale when necessary.

In all of these cases, a managed prediction service will reduce the friction of hosting and using your ML models. This guide provides an introduction to getting predictions from ML models on Vertex AI. Note that there are additional customizations, features, and ways to interface with the service that are not covered here. This guide is intended to provide an overview. For more detail, refer to the Vertex AI Predictions documentation.

Overview of the managed prediction service

Vertex AI supports batch and online predictions.

Batch prediction is an asynchronous request. It's a good fit when you don't require an immediate response and want to process accumulated data in a single request. In the example discussed in the introduction, this would be the characterizing biodiversity use case.

If you want to get low latency predictions from data passed to your model on the fly, you can use Online prediction. In the example discussed in the introduction, this would be the use case where you want to embed your model in an app that helps users identify plant species immediately.

Upload model to Vertex AI Model Registry

To use the prediction service, the first step is uploading your trained ML model to the Vertex AI Model Registry. This is a registry where you can manage the lifecycle of your models.

Create a model resource

When training models with the Vertex AI custom training service, you can have your model automatically imported to the registry after the training job completes. If you skipped that step, or trained your model outside of Vertex AI, you can upload it manually via the Google Cloud console or Vertex AI SDK for Python by pointing to a Cloud Storage location with your saved model artifacts. The format of these model artifacts could be savedmodel.pb, model.joblib, model.pkl, etc, depending on what ML framework you're using.

Uploading artifacts to the Vertex AI Model Registry creates a Model resource, which is visible in the Google Cloud console as shown below.

model resource

Select a container

When you import a model to the Vertex AI Model Registry, you need to associate it with a container for Vertex AI to serve prediction requests.

Prebuilt containers

Vertex AI provides prebuilt containers that you can use for predictions. The prebuilt containers are organized by ML framework and framework version and provide HTTP prediction servers that you can use to serve predictions with minimal configuration. They only perform the prediction operation of the machine learning framework so if you need to preprocess your data, that must happen before you make the prediction request. Similarly, any postprocessing must happen after you perform the prediction request. For an example of using a prebuilt container, see the notebook Serving PyTorch image models with prebuilt containers on Vertex AI

Custom containers

If your use case requires libraries that aren't included in the prebuilt containers, or maybe you have custom data transformations you want to perform as part of the prediction request, you can use a custom container that you build and push to Artifact Registry. While custom containers allow for greater customization, the container must run an HTTP server. Specifically, the container must listen and respond to liveness checks, health checks, and prediction requests. In most cases, using a prebuilt container if possible is the recommended and simpler option. For an example of using a custom container, see the notebook PyTorch Image Classification Single GPU using Vertex Training with Custom Container

Custom Prediction Routines

If your use case does require custom pre and post processing transformations, and you do not want the overhead of building and maintaining a custom container, you can use Custom Prediction Routines. With Custom Prediction Routines, you can provide your data transformations as Python code, and behind the scenes the Vertex AI SDK for Python will build a custom container that you can test locally and deploy to Vertex AI. For an example of using Custom Prediction Routines, see the notebook Custom Prediction Routines with Sklearn

Get Batch Predictions

Once your model is in the Vertex AI Model Registry, you can submit a batch prediction job from the Google Cloud console or the Vertex AI SDK for Python. You'll specify the location of the source data, as well as the location in Cloud Storage or BigQuery where you want the results to be saved. You can also specify the machine type you want this job to run on, and any optional accelerators. Because the predictions service is fully managed, Vertex AI automatically provisioned compute resources, perform the prediction task, and ensure deletion of compute resources once the prediction job is finished. The status of your batch prediction jobs can be tracked in the Google Cloud console.

batch prediction status

Get Online Predictions

If you want to get a online predictions, you need to take the extra step of deploying your model to a Vertex AI endpoint. This associates the model artifacts with physical resources for low latency serving and creates a DeployedModelresource.

online prediction

Once the model is deployed to an endpoint it accepts requests like any other REST endpoint, which means you can call it from a Cloud Function, chatbot, a web app, etc. Note that you can deploy multiple models to a single endpoint, splitting traffic between them. This functionality is useful, for example, if you want to roll out a new model version but don't want to direct all traffic to the new model immediately. You can also deploy the same model to multiple endoints.

Resources for getting predictions from custom models on Vertex AI

To learn more about hosting and serving models on Vertex AI, see the following resources or refer to the Vertex AI Samples GitHub repo.