Tereza Kovalenko
Exploring how AI systems behave in production environments and what we can learn from their failures
What happens when AI models leave the lab
Most people think AI performance is about accuracy scores and benchmarks. I spend my time watching what happens when these models meet real users with messy data and unexpected questions. The gap between training performance and production behavior is where the interesting problems live.
Since 2020, I've been documenting failure patterns across 47 deployed AI systems. Not the catastrophic failures that make headlines, but the quiet degradations that nobody notices until customer satisfaction drops by 12 percentage points. These patterns repeat across industries with surprising consistency.
I write about what I observe in production environments and the monitoring strategies that actually catch problems before users do. My background combines software engineering with statistical analysis, which means I care equally about system reliability and model behavior. This perspective shapes every article on Zorqali.
Areas where production data reveals the most
- Drift detection in customer-facing chatbots and recommendation engines
- Latency analysis for real-time inference systems under variable load
- Error pattern recognition across model versions and deployment contexts
- Cost optimization strategies that preserve model quality metrics
- Alert design that separates genuine degradation from noise
