Training vs. Inference: What ML Models Actually Cost to Run

This post details the cost differences between ML model training and inference, highlighting that inference costs often exceed training costs due to their recurring nature and scaling with usage. It discusses optimization techniques for inference, including quantization, pruning, knowledge distillation, and memory access optimization (e.g., FlashAttention). The post also touches on deployment strategies (batch vs. real-time) and the importance of continuous retraining and making inference outputs actionable through tools like data apps to manage lifecycle costs effectively.