If you're looking to take your first step towards digital transformation, AI is your big game changer. QASource has experts who will dive into your current business model and help you implement the best AI testing solutions for your business.
AI applications are intelligent systems built on technologies like Deep Learning, Machine Learning, Computer vision, NLP, etc.
AI model’s performance is a combination of data collection, labeling, feature engineering, model training, and re-training.
AI applications are found in a wide range of industries: Finance, Healthcare, Text analytics, Robotics, Speech Recognition, Marketing, Banking, Gaming, Autonomous Vehicles, etc.
AI is used virtually in every industry to automate monotonous business processes.
Outputs of the AI model are probabilistic and responses of the AI model for a given input can change over time.
AI works well for situations similar to that of training data but its performance is hampered in situations for which the AI model is not well-trained.
Unlike traditional QA, instead of fixed results in tests, evaluation techniques and matrices are required for AI model evaluation and testing.
Data used for training could be skewed or may suffer from class imbalance, which introduces bias in the model.
Extracting the root cause of a prediction error in AI is generally not possible as an AI model is a combination of training data, labels, algorithms, and training parameters. Therefore, narrowing down the root cause of an error is not easy.
Unlike traditional software systems, where once development and thorough QA covering all verification and validation of a feature is done, it does not require retesting until there are some new code changes, AI models need constant re-training on new data. Therefore, AI models need constant evaluation and testing.
AI/ML systems are non-deterministic, they can generate different outputs or responses for the same input on different runs. Therefore, the traditional QA approach of verifying fixed expected results does not work here.
Generating test scenarios or edge scenarios for AI systems is difficult and needs solutions like metamorphic testing.
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