Unlock the Power of Online Predictions with Vertex AI

Unlock the Power of Online Predictions with Vertex AI

Table of Contents

1. Introduction
2. Deploying a Model to an Endpoint
3. Making Online Predictions with Python
4. Making Online Predictions with REST API
5. Deploying Multiple Models to the Same Endpoint
6. Monitoring and Alerts
7. Performance and Resource Usage

Introduction

In this article, we will explore the topic of online predictions using Vertex AI, with a focus on deploying models to endpoints and making online predictions. We will learn how to make online predictions using both Python and REST API, and we will also discuss the concept of deploying multiple models to the same endpoint. Additionally, we will touch upon monitoring and alerts, as well as performance and resource usage.

Deploying a Model to an Endpoint

Before we can make online predictions, we need to deploy our model to an endpoint. Deploying a model to an endpoint associates it with physical resources so that it can serve online predictions with low latency. To deploy a model to an endpoint, we first need to create an endpoint and then select the model and version we want to deploy. We can also define the minimum number of compute nodes and the machine type for the endpoint. Once the deployment is complete, we can proceed to make online predictions.

Pros:

• Low latency online predictions.
• Ability to easily associate a model with physical resources.

Cons:

• Deployment process can take some time.

Making Online Predictions with Python

To make online predictions with Python, we can use the sample request page provided by Vertex AI. This page shows us how to make predictions using the REST API and provides a sample code that we can copy and paste into our Jupyter notebook. The code uses the Vertex AI Python SDK and allows us to define the necessary variables such as project ID, endpoint ID, and input data. By executing the code, we can make online predictions and receive the model ID and prediction results. We can also customize the input data to test different scenarios.

Making Online Predictions with REST API

If we prefer to make online predictions using the REST API, we need to ensure that we have the Google Cloud SDK installed and have authenticated our account. We can then create environment variables to hold the endpoint, project ID, and input data. By using the curl command and passing the necessary parameters, we can make a POST request to the prediction endpoint and receive the prediction results in JSON format. We can make predictions for a single item or for multiple items by providing the properly formatted JSON data.

Deploying Multiple Models to the Same Endpoint

Vertex AI allows us to deploy multiple models to the same endpoint, which can be useful when we want to test a new model on a small amount of traffic before fully switching to it. By deploying the Second model to the same endpoint, we can redirect a portion of the incoming traffic to the new model while the initial model still receives the majority of the traffic. This gradual change allows us to evaluate the performance of the new model in a production environment. We can update the endpoint configuration to specify the traffic split between the models.

Monitoring and Alerts

Vertex AI provides monitoring and alerts to help us keep track of the performance and health of our deployed models. We can enable alerts for various metrics, such as training-serving skew, which indicates a difference between the training and incoming data distributions. These alerts serve as early indicators of potential issues with the model or data. The monitoring tab provides useful charts and metrics to monitor the performance and resource usage of our endpoint.

Performance and Resource Usage

The performance and resource usage tabs in Vertex AI allow us to gain insights into how our endpoint is performing and utilizing resources. These tabs provide charts and metrics related to CPU utilization, memory usage, request count, and latency. By monitoring these metrics, we can ensure that our models are working properly and efficiently.

Conclusion

In this article, we have explored the topic of online predictions using Vertex AI. We have learned how to deploy models to endpoints, make online predictions using Python and REST API, deploy multiple models to the same endpoint, and monitor the performance and resource usage of our models. Leveraging the features and capabilities of Vertex AI, we can easily deploy and manage our models for efficient online predictions.

Resources:

• Vertex AI Documentation
• Google Cloud SDK Documentation