Scaling AI across edge, core, and cloud environments is now a top priority but also a top challenge. How do you deliver high-performance inference, maintain control over your data, and keep costs predictable across a hybrid or multi-cloud setup? In this session, you'll learn how to design and operationalize GPU-powered infrastructure that meets enterprise-grade AI demands. Whether you're leading platform architecture or responsible for AI/ML infrastructure strategy, you'll walk away with: • Proven patterns for building hybrid GPU clouds that support both training and inference • A framework for balancing performance, cost, and data sovereignty • Strategies to enable inference-as-a-service across teams and regions • Insights into how Kubernetes-native orchestration and NVIDIA technologies can accelerate deployment at scale Led by Senior DevOps Engineer Prashant Ramhit, this session offers actionable guidance based on real-world deployments. If you're building or scaling intelligent applications, this is a can't-miss discussion on how to do it right.
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