What You Need to Know About Quality Assurance for Your AI Models
Launching an artificial intelligence (AI) model that’s accurate, reliable, and unbiased is undeniably a challenge. Organizations that manage to achieve success in AI initiatives are likely aware that the quality assurance (QA) process is very different with AI than traditional QA processes. Quality assurance plays a critical role in the accuracy of an AI model and shouldn’t be overlooked. Any company hoping to deploy effective AI must build in QA checks throughout their model’s lifecycle. We often discuss the five phases of building world-class AI, which include:- Pilot
- Data Annotation
- Test & Validate
- Scaled Deployment to Production
- Retraining