8 articles in this category
Shadow deployments feel safe. You route a copy of live traffic to the new model, compare it to the champion, and wait for the metrics to tell you some...
You set up a canary: 10% of requests go to the new model, 90% to the old. Metrics look fine—latency, error rate, prediction accuracy all within thresh...
You hit deploy. Tests pass, metrics look fine. Ten minutes later, pager duty lights up—latency spikes, errors pouring in, predictions garbage. You rol...
You deploy a model. Health check passes. Life is good. Then three weeks later, a customer complains predictions are off. Your endpoint still says 200 ...
Model serving frameworks promise low-latency inference, but when a popular cache key expires, they often buckle. Cache stampedes—hundreds of concurren...
You've tuned your deployment pipeline to push thousands of inferences per second. Throughput graphs look great. Then your monitoring dashboard shows p...
You trimmed a 7B parameter model down to 4 bits. output doubled. Latency halved. But in output, the model started misclassifying rare classe it used t...
You push a new model to stagion. The canary picks it up—green metrics, low latency. Confidence is high. Then, at 25% traffic, error rates spike. You r...