MLOps.community Real LLM Success Stories: How They Actually Work // Alex Strick van Linschoten // #287
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Jan 31, 2025 Alex Strick van Linschoten, a Machine Learning Engineer at ZenML with a PhD in History, delves into practical applications of large language models (LLMs). He shares insights from his comprehensive database on LLM use cases, emphasizing both common and innovative applications. The discussion covers the technical challenges of deploying LLMs, the significance of engineering practices, and the evolution of support bots using user behavior insights. Alex also calls for community contributions to enhance collective knowledge in this rapidly changing field.
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Inspiration for LLM Database
- Alex Strick van Linschoten was inspired by Evidently AI's databases of ML/AI use cases.
- He created a similar database for LLMs, consolidating information from various sources like blogs and podcasts.
Varied LLM Use Cases
- LLM use cases are varied, unlike traditional ML, making it challenging to identify consistent value propositions.
- Many companies are simply replicating common chatbot implementations rather than exploring innovative applications.
Weights and Biases' Chatbot
- Weights and Biases shared their experience building an internal support chatbot, including their evaluation mistakes and associated costs.
- This transparency about failures is valuable for the community, but less common among larger corporations.