Monitoring and Managing Workflows
Last updated July 29, 2024
Once you've created and deployed automated workflows in Mostly AI, it's essential to monitor their performance and make necessary adjustments to ensure they operate efficiently and effectively. Proper monitoring and management allow you to identify issues, optimize workflows, and maximize their value.
Monitoring Your Workflows
- Workflow Dashboard: Review the workflow dashboard to track key performance metrics:
- Execution History: View past workflow executions, including timestamps, durations, and success/failure status.
- Trigger Statistics: Analyze how frequently triggers have been activated.
- Action Performance: Monitor the performance of individual actions within the workflow.
- Resource Usage: Track CPU, memory, and other resource consumption associated with workflow execution.
- Error Logging: Examine detailed error logs to identify any problems that occurred during workflow execution.
- Error Type: Understand the nature of errors, such as data errors, model errors, or system errors.
- Error Location: Pinpoint the specific step within the workflow where the error occurred.
- Error Timestamp: Determine when the error happened for faster troubleshooting.
- Data Visualization: Create visualizations to gain insights into workflow performance over time.
- Trend Charts: Track metrics like execution duration, resource usage, and error rates over time to identify patterns or anomalies.
- Histograms: Visualize the distribution of key metrics (e.g., prediction confidence scores) to understand performance variations.
Managing Your Workflows
- Workflow Optimization:
- Performance Tuning: Identify bottlenecks within the workflow and make adjustments (e.g., modify batch sizes or optimize execution order) to improve efficiency.
- Data Preprocessing Optimization: Tune data preparation steps to reduce processing time while maintaining data quality.
- Model Selection or Hyperparameter Tuning: Evaluate different models or refine model hyperparameters to enhance accuracy or speed.
- Workflow Maintenance:
- Update Data Sources: Ensure that data sources used within the workflow remain up-to-date or adjust connections if necessary.
- Model Updates: Retrain models periodically with new data to maintain model accuracy.
- Workflow Scaling: Adjust workflow capacity to handle increasing data volume or processing demands.
- Troubleshooting:
- Error Resolution: Address errors by debugging code, correcting data issues, or updating model parameters.
- Workflow Debugging: Utilize the platform's debugging tools to step through the workflow and identify errors.
- Rollbacks: If necessary, revert to previous workflow versions to restore functionality or undo unintended changes.
By actively monitoring and managing your automated workflows, you can optimize their efficiency, ensure their accuracy, and maximize their value within your machine learning processes.
Was this article helpful?