Deploy Trained Model with Vertex AI for Text Classification

Table of Contents:

1. Introduction
2. Deploying the Trained Model
3. Inspecting the Model Metrics
4. Changing the Confidence Threshold
5. Confusion Metrics for Model Evaluation
6. The Process of Deploying the Model
7. Deploying the Model Using Code
8. Obtaining the Model Name
9. Deploying the Model
10. Checking the Progress of the Endpoint
11. Using the Deployed Model
12. Setting Environment Variables
13. Making Sample Requests
14. Conclusion
15. Using TFX and Vertex AI Pipelines

Introduction

In this video, we will explore the process of deploying a trained model using Vertex AI. We will begin by inspecting the model metrics and changing the confidence threshold. Then, we will deploy the model using code to make it reusable for future projects.

Deploying the Trained Model

To start, we need to inspect the model and its metrics. By accessing the version number and metrics such as precision and recall, we can evaluate the performance of the model. Additionally, we can view the precision-recall curve and confusion metrics to further analyze its effectiveness.

Inspecting the Model Metrics

By checking the precision, recall, creation date, training items, and test items, we can gain a comprehensive understanding of the model's performance. This allows us to assess the model's effectiveness for our specific objectives.

Changing the Confidence Threshold

Based on our objectives, we can adjust the confidence threshold to observe how it affects the precision and recall of the model. By varying the confidence threshold, we can optimize the model for our specific use case.

Confusion Metrics for Model Evaluation

The confusion metrics provide a detailed overview of the model's performance. By analyzing the true positive, true negative, false positive, and false negative rates, we can determine the accuracy of the model in classifying different instances.

The Process of Deploying the Model

Deploying the model involves obtaining the model name and creating an endpoint. We can obtain the model name by referencing the model list and selecting the appropriate display name. Once we have the model name, we can proceed to deploy the model.

Deploying the Model Using Code

By writing code instead of using the deploy and test tab in the interface, we ensure reusability and ease of deployment for future projects. This approach allows us to train AutoML models with minimal effort and avoids the need for manual clicking.

Obtaining the Model Name

To deploy the model, we first need to obtain the model name. By copying the model's name, we can create a reference to it. This reference will be crucial for the deployment process.

Deploying the Model

After obtaining the model name, we can proceed with deploying the model. By specifying the display name of the deployed model, along with the desired synchronization options, we can create the necessary endpoint.

Checking the Progress of the Endpoint

To ensure that the endpoint is successfully created, we can monitor its progress in the interface. By navigating to the endpoint section, we can observe the status of the deployment. The endpoint needs to be in the "Ready" state before it can be utilized.

Using the Deployed Model

Once the model is successfully deployed, we can proceed to use it for predictions. By setting environment variables and making sample requests with Relevant content, we can obtain predictions based on the deployed model.

Setting Environment Variables

Setting environment variables specific to our model is crucial for accurate predictions. By exporting the necessary variables, we ensure that the model is configured correctly within our application.

Making Sample Requests

To test the deployed model, we can make sample requests with different instances of content. By providing appropriate inputs and evaluating the corresponding predictions, we can assess the model's accuracy.

Conclusion

In this video, we covered the process of deploying a trained model using Vertex AI. We explored inspecting model metrics, adjusting the confidence threshold, and evaluating the model's performance. Additionally, we discussed deploying the model using code, obtaining the model name, and checking the progress of the endpoint. Finally, we examined how to use the deployed model for making predictions.

Using TFX and Vertex AI Pipelines

If you have a custom model and want more control over the machine learning pipeline process, you can utilize TFX and deploy it on Kubeflow. However, Vertex AI pipelines offer a simpler solution by taking care of infrastructure and scaling. In a separate playlist, we will explore how to deploy TFX pipelines on Vertex AI. Stay tuned for more exciting content!

Highlights:

• Deploying a trained model using Vertex AI
• Inspecting model metrics and adjusting the confidence threshold
• Deploying the model using code for reusability
• Monitoring the progress of the endpoint creation
• Utilizing the deployed model for making predictions
• Exploring TFX and Vertex AI pipelines for custom models

FAQ:

Q: What is the advantage of deploying the model using code instead of the interface? A: Deploying the model using code makes it reusable for future projects and eliminates the need for manual interaction.

Q: How can I evaluate the performance of the model? A: By analyzing metrics such as precision, recall, and the precision-recall curve, along with confusion metrics, you can assess the model's effectiveness.

Q: Can I adjust the confidence threshold of the model? A: Yes, by changing the confidence threshold, you can optimize the model's precision and recall for your specific objectives.

Q: How do I use the deployed model for making predictions? A: By setting environment variables and making sample requests with relevant content, you can obtain predictions based on the deployed model.