You are developing a model to predict whether a failure will occur in a critical machine part. You have a dataset consisting of a multivariate time series and labels indicating whether the machine par

While Vertex ML Metadata is used for tracking artifacts and lineage, it primarily focuses on metadata storage and retrieval, not the interactive visualization of experiment data (like loss curves, metrics over epochs) that is typically implied by 'logging data and tracking artifacts' in a development context.

While Vertex ML Metadata is used for tracking artifacts and lineage, it primarily focuses on metadata storage and retrieval, not the interactive visualization of experiment data (like loss curves, metrics over epochs) that is typically implied by 'logging data and tracking artifacts' in a development context.

Creating a Vertex AI TensorBoard instance allows for visualizing and tracking experiment metrics, graphs, and other artifacts, which is crucial for iterating on preprocessing and modeling approaches. The Vertex AI SDK is then used within the notebook to log data and artifacts to the created TensorBoard instance as part of an experiment run.

This option proposes the correct components but is less complete in its description compared to option C, which clearly outlines the combined use of Vertex AI TensorBoard for tracking and the Vertex AI SDK for experiment creation.