You are developing an ML model in a Vertex AI Workbench notebook. You want to track artifacts and compare models during experimentation using different approaches. You need to rapidly and easily trans

Vertex AI Experiments provides a robust way to track and compare various aspects of ML experiments, including parameters, metrics, and artifacts, directly from a notebook using the Vertex SDK. This structured tracking facilitates informed decision-making and rapid iteration towards production-ready models.

While logging parameters and metrics is part of experiment tracking, explicitly using 'experiments' as in option A is more comprehensive for comparing models and managing iterations.

Creating a Vertex AI pipeline is for orchestrating ML workflows, not primarily for tracking and comparing individual experiment runs within a notebook for rapid iteration.

Using specific artifact types within a pipeline is part of workflow orchestration and lineage, but does not directly address the need for tracking and comparing multiple experimental runs from a notebook for rapid iteration.