Webinar: xOps in AI: Building and Operating Large-Scale Systems
September 9, 2026 · 9:00 AM – 9:45 AM PDT
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Reserve your seat. The session includes a 30-minute discussion followed by a 15-minute live Q&A.
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Modern AI systems are only as good as the data systems behind them. As organizations scale, they face growing challenges around data volume, latency, reliability, and consistency — especially when powering real-time applications and AI-driven workflows.
Join DaaX for a practical session on xOps for large-scale systems, where we'll explore how to design and operate architectures that can handle enterprise-scale demands without compromising performance or trust.
In this session, we'll break down the core principles behind building systems that are not only scalable, but also resilient, observable, and production-ready.
What we'll cover
- Key design patterns for scalable data systems
- How to ensure reliability, fault tolerance, and consistency
- Trade-offs between batch, streaming, and real-time architectures
- Handling large-scale distributed data across systems
- Best practices for monitoring, observability, and performance
- Reducing operational risk and improving system resilience
Panelists
Sajjad Khazipura
Co-Founder & CTO, DaaX
Co-Founder & CTO of DaaX. At DaaX, Sajjad is building LAKEer — an enterprise neuro-symbolic AI platform combining knowledge graphs, vector search, and structured data retrieval to deliver grounded, hallucination-resistant AI for mission-critical applications. He brings experience building internet-scale, high-reliability systems, petascale data platforms, real-time transactional systems, and industry AI solutions across e-commerce, manufacturing, healthcare, and finance. Prior roles include Principal Solutions Architect at AWS and VP & Global Practice Head at Wipro Technologies.
Subramaniyam (Sam) Pooni
DaaX Architect
Machine Learning Compiler Architect and AI Systems Leader with 30 years of hardware–software co-design experience across custom ASICs, NPUs, DSPs, and heterogeneous GPU platforms.