Operating AI at Planet Scale: Uber CTO Praveen Neppalli Naga Headlines StrictlyVC SF

Praveen Neppalli Naga's appearance at StrictlyVC is more than a keynote moment - it signals a shift in the executive conversation about AI from research novelty to operational imperative. Companies like Uber juggle real-time customer needs, safety-critical decision-making, and unpredictable traffic patterns while increasingly relying on models for routing, fraud detection, and personalization. For leaders, Neppalli Naga's perspective will illuminate the practical trade-offs between model accuracy, latency, and operational resilience.

Key takeaways for business leaders will include the importance of tightly integrated MLOps, cross-functional SRE-AI roles, and observability tailored to model behavior under production stress. Scaling AI isn't only a technical problem; it exposes procurement, cost-management, and vendor lock-in considerations. Expect guidance on how to structure teams, budget for inference at scale, and measure model performance across business KPIs rather than solely research metrics.

From a governance and risk standpoint, enterprise leaders should listen for approaches to safety, explainability, and compliance that work under real-world constraints. Neppalli Naga is likely to discuss pragmatic defenses against drift, adversarial inputs, and tooling that enables rapid rollback and canarying of models in heterogeneous environments.

Actionable next steps: assess your real-time inference costs and latency SLAs, invest in SLO-driven model observability, and create clear ownership between product, ML, and infrastructure teams. For enterprises planning to scale AI, align procurement and cloud strategy now to avoid expensive re-architecture later.