[00:00–00:07] Role of training and inference: Explanation that training creates AI models while inference generates actual business value
[00:07–00:20] Training vs. inference workflows: Contrast between training (building models from static data) and inference (running models on live dynamic traffic)
[00:20–00:27] Infrastructure mindset for inference: Discussion on how the live, continuous nature of inference demands a distinct infrastructure strategy
[00:27–00:43] Centralized vs. distributed infrastructure: Comparison between centralized GPU clusters for training and highly distributed, latency-sensitive compute for inference
[00:43–00:59] Optimal workload execution: Explanation of running the right workload in the right place across core, regional, and edge locations