Interview
summary
Focuses on the gritty post-training work: how TensorFlow vs scikit-learn require different deployment paths, efforts to optimize models after training, and practical performance comparisons between AI and traditional ML models. Calls out TensorBoard lacks interpretability for understanding how nodes detect features, notes confusion over which high-level TF library to pick, and describes cloud-serving options and multi-armed bandit traffic shifting to gradually route users toward better models rather than a single waterfall flip.
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