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Sarah Catanzaro — Remembering the Lessons of the Last AI Renaissance

Gradient Dissent: Conversations on AI

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The Biggest Bottlenecks in ML Development Today

The challenge of building software applications around a prediction service or around a model. The problem isn't like you know containerizing your model and like implementing a prediction service in production but connecting to five different databases each with different ACID guarantees. And I don't think we have great tools to facilitate that process either for ML engineers or for software engineers. Less I sound too cynical, I am really optimistic about the future of ML. We just need to do it like in a sane and rational way," he said.

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