Part 7/13:
His focus is on ensuring data consistency, with attention to fields like 'model' (to track fine-tuned versions) and 'context' (to clarify the purpose). He emphasizes the importance of fast validation loops—using try-except blocks in Python—to catch errors during data ingestion without crashing entire processes.
Performance Metrics and Data Management Concerns
Shapiro also runs performance calculations on the data generated from embeddings, illustrating how a naïve approach to storing all generated vectors could lead to immense storage requirements—potentially exceeding 20 terabytes annually at high inference rates. He calculates that each embedding is roughly 14 kilobytes, leading to large data throughput when scaled up.