AI applications often look impressive in demos but fail once they encounter real-world users, changing data, operational constraints, and governance requirements. Many teams discover these issues only after launch—when trust, reliability, and cost become production blockers rather than research problems. This talk introduces a practical framework for evaluating whether an AI system is actually ready to ship. Using a deceptively simple Retrieval-Augmented Generation (RAG) assistant as a running example, we’ll walk through the hidden gaps that prototypes frequently overlook: data freshness, hallucination handling, observability, safety guardrails, latency, scaling costs, ownership, and governance.
Rather than focusing on model architecture alone, the session reframes AI deployment as a systems and decision-making problem. Attendees will learn five production-readiness lenses that can be applied across AI products and internal enterprise systems:
The talk also explores real tradeoffs teams face in practice, including precision vs. coverage, latency vs. cost, and experimentation vs. reliability. By the end of the session, attendees will leave with a concrete checklist and decision framework they can use to evaluate AI pilots, reduce deployment risk, and move from prototype success to production reliability.
Anubha Kabra is a Senior ML research Engineer at Bloomberg. She is working on trustworthy and production-ready AI agents and systems. Her work focuses on observability, evaluation, and reliability for enterprise-scale LLM agentic applications. She has presented research at major conferences including NeurIPS, ACL, NAACL and works on building practical frameworks for deploying high-stakes AI systems responsibly.
