Enterprise GenAI initiatives hit a wall during productionization, whether they're for simple RAG apps or more complex agents. This occurs because existing data infrastructure was built for dashboards and traditional ML, not intelligent agents.
Traditional data stacks create bottlenecks when autonomous systems need task-specific data discovery, secure real-time access, and context-aware data consumption without information overload.
What You'll Learn
Join Dr. Jörg Schad, Head of Engineering at Nextdata, as he presents a developer-centric framework for AI-native data infrastructure that solves three critical challenges:
- Task-Specific Data Discovery: Design systems that provide relevant context to agents without cognitive overload from unnecessary data
- Secure Autonomous Access: Implement high-quality data access patterns that protect sensitive information while enabling real-time agent consumption
- Production-Scale Performance: Build infrastructure that handles the speed and frequency demands of autonomous data access
Key Takeaways
- Framework for transitioning from pipeline-heavy architectures to autonomous data products
- Standardized interfaces that accelerate GenAI prototype productionization
- Practical implementation patterns for internal copilots and business workflow automation
- Architecture decisions that bridge the gap between GenAI models and production data infrastructure
Who Should Attend
- Data engineers scaling GenAI applications beyond prototypes
- ML and AI engineers implementing autonomous agent workflows
- Infrastructure engineers designing AI-native data platforms
- Technical leaders evaluating data architecture for GenAI production systems
