How MLOps and LLMOps Drive Consistent Results (Kristen Kehrer)

About this episode

AI is moving quickly from experimentation to deployment, but what does it actually take to operationalize AI successfully?

In this episode of “What’s the BUZZ?”, host Andreas Welsch speaks with Kristen Kehrer about the operational realities behind deploying AI, LLMs, and agentic systems in enterprise environments.

Three key insights stand out:

• Production AI requires more than a successful demo
   Many organizations underestimate the complexity of moving AI systems into production. Reliable deployment requires monitoring, governance, iteration, and collaboration across business and technical teams.
• Clean data and knowledge bases remain essential
   Even advanced AI systems depend on high-quality documentation and structured information. Weak knowledge bases often lead to unreliable outputs and poor user experiences.
• LLMOps introduces a new operational layer
   Managing prompts, retrieval pipelines, evaluations, and interaction quality has become critical as organizations scale customer-facing AI systems and AI agents.

A practical reminder: successful AI adoption is not about deploying the newest model first. It is about building reliable systems, strong operational processes, and the right collaboration between people and technology.

Listen to the full episode for a grounded perspective on what it takes to operationalize AI at scale.

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Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers.

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