How Do You Test an AI Model? 🤖🧪

Traditional software testing verifies deterministic behavior. AI models? They deal in probabilities, patterns, and uncertainty. 

Testing AI systems requires a shift in mindset. This workflow illustrates how to test AI models:

Key Takeaways:

✔ AI testing starts with data — not code 

✔ Accuracy is only one dimension of validation 

✔ Robustness & adversarial testing are critical 

✔ Governance and ethics are non-negotiable 

✔ Monitoring is continuous, not one-time 

✔ Humans remain essential in the loop 

AI systems don’t just need testing. They need AI assurance frameworks integrated into DevOps + MLOps pipelines. 

The future of Quality Engineering includes: • Model validation • Risk-based AI controls • Continuous compliance • Responsible AI governance 

👉 How is your team implementing QAOps and AI Testing today? 

📩 At Keas Group LLC, we help firms bring AI into their QA strategy. Contact us at info@keasgroup.com or visit www.keasgroup.com.