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As an ML researcher evaluating multiple deep learning model architectures and hyperparameter configurations, you need to implement a robust solution to track each model iteration, visualize key metrics, gain insights into model internals, and optimize training performance.
To ensure your solution has the most efficient and powerful approach for comparing models and the strongest visualization capabilities, how should you build this solution?