Abstract
Details coming soon.
Inside an In-House Data Agent
From the same track
Session
Progressive Failure Modes of Modern AI Serving Systems
Inference platforms fail in layers. Most organizations focus on model quality while underestimating the systems engineering required to operate production AI workloads safely and reliably at scale.
Abi Aryan
AI Infrastructure Engineer and Educator
Session
The Revenge of the Data Scientist: Why Reliable AI Needs Evals, Traces, and Metrics
Most teams can now ship an AI prototype by calling a foundation-model API. The hard part is knowing whether that system works when real users, messy data, and business consequences arrive.
Hamel Husain
Machine Learning Engineer, 20+ Years in Applied AI, Machine Learning, and Data Science
Session
Skills, Memory, or Fine-Tuning? The Engineering Loop Behind Self-Improving Agents
As agents become mainstream, everyone wants to improve theirs either by making fewer mistakes on existing tasks or by taking on harder ones. This usually happens once an agent is already deployed in production.
Abhinav Sinha
CEO @Lucidic AI, Previously @Stanford AI Lab, @Citadel and Susquehanna International Group, and @Apple
Session
Performance Engineering in the Age of AI
Details coming soon.