Over 75% of the technology and business executives agree that they need to be using some form of scalable AI across business functions to stay competitive. AI includes ML, Predictive & Big Data Analytics, Computer Vision, Natural Language Processing (NLP), and much more. Creating a proof-of-concept or fully functional AI/ML implementation is only a part of the challenge. Organizations need to have a reliable testing strategy and a comprehensive testing approach to scale the business outcome. Testing AI/ML-based systems are much more complex than traditional software.
AI/ML systems are non-deterministic; they can exhibit different behaviors for the same input on other runs.
ML models depend on a large amount of accurately labeled data. Preparation of this training dataset takes about 80% of a data scientist’s time.
The distribution of the training dataset may introduce unwanted bias in the AI systems.
Extracting specific attributes that caused a behavior or inference is very difficult. For example, finding what caused a system to recognize an image of a coupe as a sedan wrongly may not be possible.
Once we thoroughly test and validate traditional software systems, we do not need to retest them until we modify the software. However, AI/ML systems constantly learn, train, and adjust themselves with new data and input. Only a sustainable testing capability can ensure AI/ML systems produce results that appeal to human intelligence.
Implement an Artificial Intelligence/Computer Vision (AI/CV) powered experience for your business by leveraging the best practices from actual deployments.
We provide services to curate, label video, image, and textual data, audit, and verify the test data used by your AI system. We use AWS Sage Maker Ground Truth platform to orchestrate these services. We perform data accuracy testing to validate if the data represent the real world well enough. We also validate the dataset for any inherent bias.
To establish the trustworthiness of AI systems, the AI/ML models need to be validated. We divide the data into testing(holdout), training, and validation sets and perform various cross-validation tests on different smaller subsets of the test datasets. We provide feedback to the model developers for the improvement of the model.
We need to conduct scalability, performance, and security testing for the AI/ML systems. Many AI/ML systems are accessed through API layers by interactive user systems. We will ensure privacy and security are not compromised through these external systems. Non-functional testing will also produce operational metrics such as inference speed, classification accuracy, mean absolute error.