What are the main challenges of hybrid validation in AI systems?
Hybrid validation combines different validation methods, which can lead to complexity in integration and consistency.
Can you explain why consistency is a challenge?
Consistency is challenging because different validation methods may yield conflicting results, making it hard to determine the best approach.
How can we address these challenges?
Addressing these challenges involves establishing clear protocols, using standardized metrics, and ensuring effective communication among teams.
Are there specific tools that can help with hybrid validation?
Yes, tools like MLflow and TensorBoard can help manage experiments and visualize results, aiding in hybrid validation.
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