When developing AI solutions, don’t just chase higher accuracy—optimise resource efficiency as well but don’t sacrifice accuracy for speed. A 99% accurate model that takes hours to run is often less valued than a 95% model that delivers real-time results but look at what the consequences of sacrificing accuracy might be.
🚀 Pro Tips:
✅ Use quantisation & pruning to reduce model size without major accuracy loss.
✅ But beware that using pre-trained models instead of building from scratch can compound built-in biases
✅ Leverage vector databases for faster retrieval in NLP applications.
💬 Do you believe sacrificing accuracy for faster speeds can cause long-term ill effects?
