Dive deeper into the world of Generative AI (GenAI) with Goran Cvetanovski, CEO and Founder of Hyperight, and Dr. Kjell Carlsson, a seasoned AI Evangelist and Head of Data Science Practice at Domino DataLab, in part two of this insightful video interview series. This episode, "Making Generative AI Real," focuses on the practical aspects of operationalizing GenAI applications at scale, unraveling the complexities and challenges that organizations face in bringing GenAI from experimentation to reality.
Part One of the Interview: [ Ссылка ]
Key Topics and Deep Dives:
Hitting a Wall with GenAI:
- Uncover the paramount challenges inhibiting companies from reaping value from GenAI.
- Understand the complexities and stumbling blocks in constructing real-world GenAI applications.
The Outsourcing Dilemma in GenAI:
- Explore why organizations cannot simply outsource their GenAI models.
- Dive into the limitations of prompt engineering and RAG, and why they might not be the silver bullet for GenAI applications.
Operationalizing GenAI – A Necessity Not a Choice:
- Learn why having capabilities for operationalizing GenAI applications is not just beneficial but essential.
- Discuss the nuances of bringing GenAI models from the lab to the real world, ensuring they are scalable, maintainable, and deliver on their promise.
LLMOps vs. MLOps – A Detailed Analysis:
- Explore the key capabilities required for operationalizing GenAI models and how they diverge from traditional ML models.
- Engage in a thought-provoking discussion on whether LLMOps should be considered its own set of capabilities.
- Weigh the pros and cons of considering LLMOps and MLOps as separate entities, and what that means for organizations striving to implement GenAI effectively.
Governance in GenAI:
- Delve into the critical aspect of governance in implementing and managing GenAI applications.
- Explore strategies to ensure that GenAI applications are not just effective but also ethical, responsible, and compliant with relevant regulations.
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