ML pipelines and reproducibility
21 practice items · needed by 2 roles
Easy 7Medium 6Hard 8
Practice questions
- Great offline, bad in productionhard
- What a model registry is foreasy
- Why version dataeasy
- What experiment tracking recordseasy
- Same code, different resultseasy
- Logging a metric every epocheasy
- Making a training run repeatableeasy
- Bad data reached trainingeasy
- When to retrainmedium
- Online and offline feature storesmedium
- Safe retries in pipelinesmedium
- Joining features to training labelsmedium
- Deploying by alias, not versionmedium
- Online features stopped updatingmedium
- Rollback that made things worsehard
- Same seed, different resultshard
- Loading a model from a public hubhard
- Daily batch missing late eventshard
- Pipeline reused yesterday outputhard
- Streaming counts that run highhard
- Loss spike after resuming traininghard