Quality Assurance of Artificial Intelligence Systems
Reading Time: 2 minutesWith increasing adoption of AI in software and cyber-physical systems, the system behaviour is rapidly evolving from a rule driven response to intelligence driven response. Such a response is dynamic and data driven and is never deterministic thus rendering the conventional testing and monitoring paradigms ineffective. Moreover, since the system behaviour is autonomous at run time, there are ethical, transparency, regulatory and compliance issues that need to be validated, monitored and assured before the system is deemed fit for production In this webinar, Mallika explains the business and technical challenges in developing, testing and continuously monitoring AI…....
You need to Register or to access the full content.
- IT Leader with 20 years of extensive Innovation and Quality Engineering experience in the IT industry
- Leads Artificial Intelligence in Testing. Expertise in application of Cognitive Computing and Machine Learning models in Quality Engineering.
- Leads the Asset Development cluster for Testing, driving the latest automation innovations and AI powered tooling
- Holder of patent for innovations in Quality Engineering.
- Speaker at various International conferences like A2IC International AI conference 2018, Barcelona and O’Reilly AI conference 2018, NY
- Project & Program management expertise – Test Strategy & Planning, Risk Management, Test Metrics & SLA definition and tracking, Unit of Work pricing models, Risk based testing, Automation ROI analysis, Knowledge Management