ML Pipeline
Discover how our ML pipeline models device event logs to reproduce severity-1 crashes and reduce mean time to resolution.
CHALLENGE
When millions of customers depend on your devices, high‑severity crashes aren’t just bugs; they’re brand‑damaging crises. Existing crash‑reproduction methods relied on random trials, making fixes painfully slow. Severity‑1 crashes were difficult to reproduce because the triggering sequence of events was unknown, and engineering teams spent days guessing.
SOLUTION
We developed a machine‑learning‑driven solution. By modelling the problem with hidden Markov models and recurrent neural networks, we built a training and inference pipeline on AWS. The pipeline ingests device event logs and generates the most probable sequence of events to reproduce the crash. A web interface makes it accessible to developers across teams.
Impact
The system returns a single sequence of steps that maximises the probability of reproducing a given crash, dramatically reducing mean time to resolution and engineering hours. It has lowered the frequency of severity‑1 incidents and established a reusable pattern for applying machine learning to reliability problems.
