AI 101: Building and Deploying an AI Model

AI model training involves data gathering, cleansing, training, checkpointing, and deployment. This complex process is resource-intensive and time-consuming, with data preparation taking up to 80% of development time. Training uses high-performance hardware to process data iteratively. Checkpoints capture the model's status during training. Once complete, the model is exported and deployed. Inference compares new data to the trained model to generate responses, with additional filters potentially improving output quality.