Vertex Explainable AI vs. Kubeflow

Understand AI output and build trust. Explainable AI is a set of tools and frameworks to help you understand and interpret predictions made by your machine learning models, natively integrated with a number of Google's products and services. With it, you can debug and improve model performance, and help others understand your models' behavior. You can also generate feature attributions for model predictions in AutoML Tables, BigQuery ML and Vertex AI, and visually investigate model behavior using the What-If Tool.

Kubeflow is an open-source platform designed to facilitate the deployment, orchestration, and management of machine learning (ML) workflows on Kubernetes. It provides a comprehensive suite of tools that cover the entire ML lifecycle, enabling data scientists and engineers to develop, train, and deploy models efficiently in scalable and portable environments. Key Features and Functionality: - Kubeflow Notebooks: Offers web-based development environments, such as Jupyter Notebooks, running inside Kubernetes pods, allowing for interactive model development.