Assistance Required with Enabling Explainable AI for Multi-Label Image Classification Model on Verte

[linda_wei]

Despite thoroughly reviewing the documentation and exploring the Vertex AI Console, I have encountered difficulties in finding an option to enable Explainable AI during the model training, deployment, or while making online predictions. This feature is crucial for my project as it aids in understanding the model's decision-making process, thereby ensuring transparency and trustworthiness in the model's predictions.

Here are the specific steps I've taken and the challenges I've encountered:

Model Training: While creating a multi-label image classification model on Vertex AI, I looked for options or configurations to enable Explainable AI features but could not find any relevant settings. Model Deployment: Similarly, during the model deployment process, I was unable to locate any settings related to enabling Explainable AI or specifying explanation parameters. Online Predictions: I also attempted to find explanations options when using the model for online predictions but to no avail. Given these challenges, I would greatly appreciate your guidance on the following:

Are there specific steps or configurations required to enable Explainable AI for multi-label image classification models on Vertex AI? If so, could you please provide detailed instructions or point me to the relevant documentation? Are there any prerequisites or limitations I should be aware of when using Explainable AI with multi-label image classification models?

[Poala_Tenorio]

Enabling Explainable AI features in Vertex AI for multi-label image classification models is indeed crucial for transparency and understanding the model's decision-making process. While Vertex AI provides powerful capabilities for model training and deployment, enabling Explainable AI may require additional steps.

Here's a guide on how to enable Explainable AI for multi-label image classification models on Vertex AI:

1. Model Training:

   • While creating your training job using Vertex AI, ensure that you are using a compatible model type that supports Explainable AI. Not all models may support explanation features.
   • Look for options related to model explainability during the model configuration step. This might involve specifying additional parameters or enabling certain settings related to explainability.

2. Model Deployment:

   • After training your model, when deploying it on Vertex AI, explore the deployment options for any settings related to explainability.
   • There might be specific configurations during deployment where you can enable explanation features or specify explanation parameters. Ensure that you select the appropriate settings to enable explanation features for your deployed model.

3. Online Predictions:

   • When making online predictions using your deployed model, there should be options to request explanations along with predictions.
   • Explore the prediction APIs or interfaces provided by Vertex AI to specify that you want explanations for the predictions.
   • Depending on the implementation, you may need to make additional API calls or include specific parameters to request explanations.

4. Documentation and Support:

   • Refer to the official Vertex AI documentation for detailed instructions and guidance on enabling Explainable AI features for multi-label image classification models.
   • If you encounter difficulties, consider reaching out to the support channels provided by Google Cloud Platform for assistance.

5. Prerequisites and Limitations:

   • Be aware of any prerequisites or limitations associated with using Explainable AI with multi-label image classification models. These could include model type compatibility, additional computational resources required for generating explanations, or restrictions on certain features.