Knowledge Graph — Coursera Notes › Academic disciplines › Information Technology / Computer Science › Cloud Computing › Snowflake › AI/ML › Model Deployment
Troubleshooting
concept · part of Model Deployment
Troubleshooting is a systematic process of identifying, diagnosing, and resolving problems or issues within a system, device, or process. It is important because it enables efficient problem resolution, reduces downtime, and improves overall system reliability and user satisfaction.
Inside Troubleshooting (5)
- Canary deployment — A deployment strategy that minimizes risk by releasing a new model incrementally to a small group of users before full-scale deployment.
- Data validation and preprocessing debugging — Troubleshooting data quality issues that affect model performance.
- Monitoring and alerting — Monitoring deployed model performance to detect problems early, with alerting mechanisms to notify teams when metrics fall below or exceed thresholds.
- Performance profiling — Performance profiling analyzes system performance to understand why a model may be underperforming in latency or computational efficiency.
- Root cause analysis — Root cause analysis (RCA) is a systematic process for identifying the primary causes of an issue, aiming to eliminate the root cause to prevent recurrence.
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