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.
Our bloggers are the test management experts at QASource. They are executives, QA managers, team leads, and testing practitioners. Their combined experience exceeds 100 years and they know how to optimize QA efforts in a variety of industries, domains, tools, and technologies.
To get a comprehensive coverage of your software testing requirements, opt for our