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Best Practices for Putting LLMs into Production

Key Takeaways:
  • How to successfully get AI into production
  • How can models be trained cheaper and more efficiently
  • When should I start to care about GPUs? How can they be used efficiently?
Thursday November 23, 11AM ET
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The webinar aims to provide a comprehensive overview of the challenges and best practices associated with deploying Large Language Models into production environments, with a particular focus on leveraging GPU resources efficiently. The discussion will discuss effective strategies for optimizing AI model training to reduce costs, thereby facilitating wider adoption of AI technologies across diverse business scales. Further, we will dive into the practical and strategic aspects of GPU utilization, the transition from single to clustered GPU configurations, and the role of evolving software technologies in enhancing GPU-based training capacities. The webinar also aims to highlight how businesses of different sizes can approach these transitions to gain a competitive edge in an AI-driven market. Through a blend of theoretical insights and practical examples, attendees will garner a clearer understanding of how to navigate the complexities involved in moving LLMs from development to production stages.

Presenter Bio

Ronen Dar Headshot
Ronen DarCo-founder and CTO at Run:ai

As CTO at Run:ai, Ronen is helping build an infrastructure platform for enterprises to manage AI workloads and simplify their AI journey. He has previously worked as an engineer and researcher at Apple, Intel, Bell Labs, and Tel Aviv University.

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