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We are back with session #4 of AI Broadcast series, our fortnightly event series where we explore the latest advancements, trends, and implications of AI. 🎉 Join us as we take a deep dive into the world of LLMOps. 🚀 Since their debut in 2023, LLMs have been reshaping industries, sparking innovation, and driving new possibilities. But with great power comes great complexity. As enterprises delve into LLM adoption, they're realizing the need for a strategic approach. Enter LLMOps – the backbone of deploying LLM applications at scale. It's not just about building models; it's about orchestrating the entire lifecycle seamlessly. Mark your calendars and join the conversation on Friday, 21st June 2024! #LLMs #LLMOps #AI #AIBroadcast #AryaXAI https://lnkd.in/dywkRu7B

AI Broadcast series #4: The evolving LLMOps stack | Event by AryaXAI

AI Broadcast series #4: The evolving LLMOps stack | Event by AryaXAI

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Godwin Josh

Co-Founder of Altrosyn and DIrector at CDTECH | Inventor | Manufacturer

6mo

The emergence of LLMOps marks a pivotal shift in AI deployment strategies, emphasizing the importance of holistic lifecycle management. As LLMs continue to reshape industries, the complexity of deploying and managing them at scale becomes increasingly apparent. However, strategic approaches like LLMOps offer a framework for orchestrating this complexity effectively, ensuring seamless integration and operation of LLM applications. You talked about the significance of LLMOps in your post. How do you envision leveraging LLMOps to address challenges specific to your organization's AI initiatives? If you imagine deploying LLMs in a highly regulated industry like healthcare, how would you technically use LLMOps to ensure compliance and maximize performance in such a scenario?

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