Data scientists in the modern-day are often called the Sherlock Holmes of the tech world. Why, you may ask? When it comes to detecting Large Language Model hallucinations, it calls for a keen eye that can unravel and understand the why and how behind complex model predictions. And as they say, with great power comes greater responsibilities.
Navigating the biases, hallucinations, and drifts of LLMs in production can be quite the herculean task for AI teams. Without continuous monitoring, these massive models can pose significant risks of losing revenue opportunities, reputational damages, and regulatory non-compliance.
Leveraging real-time monitoring practices with embedding visualizations for sentiment analysis and performance tracing of text meta features could help uncover model issues, detect hallucinations and trends in your NLP and Generative AI models.
Join a session with our AI expert, Devanshi Vyas, Co-founder at Censius where she discusses key challenges of unmonitored Language models in production and explores strategies for risk minimization of AI models to improve model’s health, performance, and reduce operational expenditure.
Key takeaways
– Key complexities and challenges of Large Language Models (LLMs) in production
– Need for continuous risk monitoring in LLMs to improve visibility and curb AI risks
– Role of AI Observability with embedding visualizations and performance tracing to proactively monitor the performance of Language models
Technical level: High Level/overview
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