AI training vs. inference: what's the difference?

The text explores the distinct phases of AI model development, focusing on training and inference. Training involves teaching a model through exposure to extensive datasets, adjusting its weights to learn patterns and relationships, and may include a fine-tuning stage for specific tasks. In contrast, inference is the phase where the trained model generates outputs in response to new data, a process characterized by different hardware requirements and cost structures. The lifecycle of a model includes pre-training, post-training fine-tuning, optimization for specific hardware, deployment, and serving, with specific metrics like time to first token (TTFT), time per output token (TPOT), throughput, and latency being crucial for assessing inference performance. The text also details how Baseten, an inference platform, optimizes and facilitates AI deployment, offering solutions for custom models and automating infrastructure management, thus allowing teams to focus on model performance without dealing with the technical complexities of deployment and scaling.