The rise in adoption of Artificial intelligence-powered applications has led to demand for Quality engineering for AI and Machine learning applications. AI-based systems are non-determinism and provide probabilistic outputs as compared to fixed outputs in traditional software systems. Therefore, Quality assurance for AI applications can’t be effectively achieved through traditional QA methods. The right AI QA strategy requires methods that can evaluate the Model’s accuracy and check variance in results.
