Delve into the evolution of machine learning (ML) from research to production with Josh Tobin, co-founder and CEO of Gantry, in this enlightening discussion on "The Deep Learning Podcast by Deci." Drawing from his extensive experience, including a PhD in Computer Science at UC Berkeley and his role as a research scientist at OpenAI, Tobin provides valuable insights into the transition of ML from academic research to real-world applications.
Key Highlights:
Guest Introduction: Meet Josh Tobin, as he shares his journey from academia to entrepreneurship, highlighting his expertise in MLOps and the practical aspects of deploying ML models in production.
ML in Production: Explore the significant differences between ML in a research setting and ML in production, emphasizing the importance of integrating ML models within broader product systems.
Emerging Trends: Tobin discusses the emerging field of MLOps, the impact of foundational models like GPT-3 on ML operations, and the nuanced challenges of deploying AI systems in real-world scenarios.
Practical Considerations: Gain insights into practical aspects of ML in industry, including experiment management, feature stores, and the complexities of integrating state-of-the-art models into production systems.
Future Outlook: Tobin offers advice for practitioners and businesses navigating the AI transformation, stressing the collaborative potential between humans and AI and underlining the critical role of prompt engineering in the next generation of AI applications.
Join us in this engaging conversation with Josh Tobin, as we explore the dynamic landscape of ML research, production, and the future trends shaping the field of artificial intelligence and machine learning.
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