Optimizing AI Performance: Identifying Bottlenecks in Data Pipelines
When companies build their first AI solutions, they pour resources into making models more sophisticated without addressing the choke point that caps performance. Product teams often make this same error by missing where the constraint lies, which might be in their data pipeline struggling to keep up with GPU cycles, or in preprocessing that takes longer than inference, rather than in model architecture. Sometimes their annotation workflow creates significant lag before new examples reach training. Their entire pipeline typically can…



















