The text discusses the critical differences between AI training and inference, emphasizing their unique roles, requirements, and impacts on AI projects. Training involves feeding labeled data into a model to learn patterns, requiring substantial computational resources, often using clusters of GPUs or TPUs, and is performed periodically. In contrast, inference applies these learned patterns to new, unseen data to make real-time predictions, necessitating low-latency responses and running continuously, which can lead to higher cumulative costs over time. Both phases are essential but serve distinct purposes: training focuses on model learning, while inference emphasizes prediction and user interaction. Understanding these differences is vital for optimizing AI systems to ensure they are cost-effective, efficient, and deliver a smooth user experience. By treating training and inference as separate engineering challenges, organizations can better allocate resources, optimize costs, and enhance the performance of AI systems through methods such as quantization, pruning, and dynamic batching.