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.
Learning Objectives:
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.
