Full Stack Data Science
The discussion emphasizes the importance of having full stack data scientists who can handle all aspects of data projects, from ETL to A/B testing. As data and company strategies evolve, it's crucial to adapt objectives and ensure effective coordination among team members. Flexibility and continuous support are key to navigating the dynamic nature of data-driven products.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Live from TWIMLcon! Culture & Organization for Effective ML at Scale (Panel) - #308
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