198: Building AI Search and Customer-Enabled Fine-Tuning with Jesse Clark of Marqo.ai
The Data Stack Show
From Physics Experiments to Machine Learning Applications at Amazon
The chapter explores the guest's background in experimental physics and their journey into machine learning post PhD, discussing the challenges of working with big data in physics experiments and the evolving tools and technologies in the field. It highlights the transferability of skills from experimental data analysis to machine learning applications, emphasizing the importance of continuous learning to bridge interdisciplinary gaps. The conversation delves into the success of building AI algorithms at a company, touching on the key factors like diverse team expertise, freedom for exploration, and dedication driving data science success.
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