The surging demand for Natural Language Processing (NLP) and Large Language Models (LLM) has broken glass ceilings across industries in recent years. In fact, as per Fortune Business Insights, the global NLP market is projected to reach $161.81 billion by 2029, suggesting its importance to businesses and the broader economy as the volume of unstructured data increases. 

As we become more reliant on NLP in our daily lives, it’s crucial to be aware of the potential risks that come with it. In the absence of monitoring, these models are prone to causing unintended consequences and posing significant risks to businesses, from reputational damage to regulatory non-compliance.

Even the best human minds need sharpening, and the same goes for complex NLP models. Implementing embedding monitoring practices can help detect and reduce drift and bias, understand black-box model decisions, improve model performance, and minimize technical debt.

In this session, Devanshi Vyas, Co-founder at Censius will deep dive into the implications of unmonitored NLP and LLM in today’s world. Explore the key components of AI Observability, including Monitoring and Explainability, and how they can be applied to drive positive business outcomes.

Key takeaways: 

• Explore the current state of unstructured data models possessing massive complexities
• The need for Observability in NLP to improve visibility and curb AI risks
• Strategies to leverage AI Observability with embedding monitoring to proactively troubleshoot models for optimal performance