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Data Quality, DataOps, and Large Language Models

Struggling to bring order to the data and AI chaos?

The reality for many data teams is often an unproductive mix of broken pipelines, reactive problem-solving, and “good enough” data that leads to poor decisions. With the introduction of Large Language Models (LLMs), the situation has become even more complicated, increasing the number of data use cases, generating “vibe” data engineering projects, and intensifying the confusion. However, there is hope. 

In this session, we demonstrated how the proven principles of DataOps — including agile iteration, lean efficiency, and DevOps-style automation — can help restore sanity to your data stack and improve the quality of your LLM code and insights.

Watch now for a candid, humorous, and practical discussion on how to provide your AI with a healthy data diet, even on a limited budget.