BinaryBourbon

// Hard-Won Production Insights

• Most AI problems are actually data pipeline problems - 80% of production failures come from data drift, not model performance
• A well-tuned BERT model often beats GPT-4 for specific tasks at 1/1000th the cost - know when to use a sledgehammer vs a scalpel
• Caching embeddings properly can reduce inference costs by 95% - most production requests see repeated inputs
• The difference between a PoC and production ML is error handling - your model will see inputs you never imagined
• Quantization isn't just about memory - int8 inference can be 4x faster on modern hardware with <2% accuracy loss
• Real-time feature computation kills more ML projects than model accuracy - precompute everything you can
• The best model is the one that ships - I've seen too many teams optimize F1 scores while competitors capture markets