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Dean and Natanel discuss AI and ML topics like model efficiency, data quality, APIs vs. self-hosting, and success metrics. They explore data scientists’ evolving role with LLMs, collaboration with product teams, and the future of robotics in AI.​
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In this episode, Dean and Natanel Davidovits explore the intricacies of AI and machine learning, focusing on model efficiency, the use of APIs versus self-hosting, and the importance of defining success metrics in real-world applications. They discuss the challenges of data quality and labeling, the evolving role of data scientists in the age of LLMs, and the significance of effective communication between data science and product teams. The conversation also touches on the future of robotics in AI and the need for specialization in a rapidly changing landscape.
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