LLM Observability 101: Common Challenges Seen in Production



In today’s hyper-connected world, the pace of AI development and innovation is staggering, reshaping industries and redefining what’s possible at unprecedented speed. According to a recent survey, over half (53%) of data science and machine learning teams say they plan to deploy large language model (LLM) applications into production in the next 12 months or “as soon as possible” – however, nearly as many (43%) cite issues like accuracy of responses and hallucinations as a main barrier to implementation. 

In this Lunch & Learn, we will discuss how to best alleviate the challenges machine learning and data science teams face when implementing LLMs in production. 


  • ​Understand the landscape of AI innovation, including LLMs, and its transformative potential 
  • ​Discover the foundational technologies required to build robust and resilient LLM infrastructure
  • Deep-dive into the world of word embeddings, learning how these vector representations are fundamental to the operation of language models.
  • Understand where issues generally emerge with LLMs in production, their causes, and implications for your LLMOps practice. 
  • Introduction into strategies for monitoring, troubleshooting, and fine-tuning LLM models.

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Sally-Ann DeLucia
ML Solutions Engineer
Hakan Tekgul
ML Solutions Engineer

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