Understanding Model Predictions with Vertex Explainable AI

🔍Vertex Explainable AI allows you to analyze and interpret your machine learning model's predictions by assessing explanations and feature attributions.

🌍Explainable AI provides more context and secondary information to understand differences in the dataset and how the model acts.

📊Understanding model predictions is crucial for model builders, data scientists, ML Ops, end users, and public stakeholders to evaluate and interpret machine learning models.

🧪Explanations help verify model behavior, recognize bias, and improve model training and data.

🚀Vertex Explainable AI offers insights to evaluate ML model predictions and develop guidance for responsible use of AI.

What is Vertex AI?

—Vertex AI is Google Cloud's end-to-end machine learning platform that helps accelerate machine learning experimentation and deployment.

How does Vertex AI help with model interpretation?

—Vertex Explainable AI allows you to analyze explanations and feature attributions to better understand your model's predictions and behaviors.

Who can benefit from Vertex Explainable AI?

—Model builders, data scientists, ML Ops, end users, and public stakeholders can all benefit from Vertex Explainable AI to evaluate and interpret machine learning models.

What insights can Vertex Explainable AI provide?

—Vertex Explainable AI provides insights to verify model behavior, recognize bias, and improve model training and data.

How can Vertex Explainable AI contribute to responsible AI use?

—By offering insights into model predictions and helping develop guidelines, Vertex Explainable AI can support responsible use of AI.

00:03In this video, Chloe Condon provides an overview of Vertex Explainable AI and its role in understanding model predictions.

00:39Vertex Explainable AI allows you to analyze and interpret your machine learning model's predictions through explanations and feature attributions.

01:35Explainable AI provides more context and secondary information to understand differences in the dataset and how the model acts.

03:27Understanding model predictions is crucial for model builders, data scientists, ML Ops, end users, and public stakeholders to evaluate and interpret machine learning models.

04:19Explanations help verify model behavior, recognize bias, and improve model training and data.