Which platform is primarily used for visualizing large-scale model training progress?

The correct choice highlights an essential tool in the field of machine learning and model development. Nvidia TensorBoard is specifically designed for visualizing various aspects of training machine learning models, including large-scale models. It provides a suite of visualizations that help users track metrics such as loss and accuracy over time, visualize the computational graph, and inspect individual layers and their outputs.

This platform is widely utilized because it enables developers to gain insights into their models' training processes, allowing them to make informed adjustments as needed. Features like real-time updating of graphs, comparison of different runs, and streamlined integration with TensorFlow make TensorBoard a powerful ally for any practitioner working with large-scale models.

In contrast, while platforms like NeMo Guardrails, MLflow, and Prometheus serve important roles in the machine learning ecosystem—such as workflow orchestration, experiment tracking, and system monitoring, respectively—they do not primarily focus on the visualization of training progress in the same way that TensorBoard does.