• Assessing how to evaluate readiness for AI workloads
• Understanding the importance of a mindset shift beyond the technology
• Designing data flow topologies
• Embed real-time intelligence across applications
• Ensuring resilience and observability throughout the stack
• Optimizing distributed compute and integrating emerging AI accelerators.
• Practical lessons learned from managing model lifecycle sprawl