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.
In recent years, conventional SaaS models have evolved into sophisticated service-oriented platforms geared toward enhancing business efficiencies and capabilities between the company and its suppliers, clients, and business partners.
Testing machine learning based applications or artificial intelligence systems requires a different approach compared to testing traditional software systems. This is because traditional software systems.
Smartphones, smart speakers, smart cars, smart coffee makers... the list goes on. It seems like everything around us is coming to life and getting intelligent. And though the sci-fi genre thrives on.
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AI testing involves the verification of artificial intelligence and machine learning-based products to improve the quality, efficiency, and effectiveness of such software products. It includes data validation, model evaluation, and testing of AI applications across various industries.
Testing AI applications is fundamentally different due to their probabilistic nature, reliance on vast amounts of data, and the continuous learning aspect. These factors require unique approaches, such as metamorphic testing and continuous evaluation metrics.
QASource uses advanced techniques and methodologies, such as metamorphic testing, confusion matrix, AUC ROC, and F1 Score metrics for model evaluation, to ensure comprehensive testing of AI applications. Our approach is tailored to meet the specific needs of each AI application, whether it involves NLP, computer vision, robotics, or any other AI technology.
You can begin by scheduling a 30-minute free consultation with one of our AI specialists. We'll discuss your current business model, understand your testing needs, and recommend the best AI testing solutions for your business.
Key challenges include dealing with non-deterministic outcomes, identifying bias, ensuring data quality, and creating relevant test scenarios. QASource tackles these with advanced testing methodologies, continuous learning approaches, and AI-driven testing tools.