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AI Infrastructure at Scale – Building the Backbone for Real-Time Intelligence
As AI adoption accelerates, infrastructure becomes a strategic differentiator—not just a technical necessity. This masterclass explores how to architect scalable, production-grade AI systems that support real-time intelligence, continuous learning, and operational agility. Attendees will learn how to align infrastructure decisions with business priorities, regulatory demands, and innovation roadmaps.
What You’ll Learn
- How to design scalable, cloud-native AI pipelines using Kubernetes, Ray, and vector databases to support enterprise-grade workloads.
- Strategic approaches to real-time inference, edge deployment, and model serving for latency-sensitive applications.
- Best practices for monitoring, retraining, and cost optimization to ensure infrastructure remains agile, compliant, and future-ready.
Key Takeaways
- A readiness checklist and deployment templates to accelerate infrastructure planning and execution.
- Tools and frameworks for observability, model drift detection, and performance tuning in production environments.
Why This Matters
AI infrastructure is the foundation for enterprise-scale intelligence. Without robust, scalable systems, organizations risk bottlenecks in performance, compliance, and innovation. This session equips technical and strategic leaders with the tools to future-proof their AI infrastructure and unlock long-term value.
Who Should Attend
ML engineers, DevOps teams, platform architects, and AI product leads working in:
- SaaS & Cloud
- Fintech
- Telecom
- Logistics
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