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Duration 14 hours
Course Outline
Fundamentals of Quality and Observability in WrenAI
- The importance of observability in AI-driven analytics
- Key challenges in assessing NL-to-SQL outputs
- Establishing frameworks for quality monitoring
Assessing NL-to-SQL Precision
- Setting success metrics for generated queries
- Creating benchmarks and testing datasets
- Automating evaluation workflows
Prompt Optimization Strategies
- Refining prompts for enhanced accuracy and efficiency
- Adapting prompts to specific domains
- Managing prompt repositories for enterprise environments
Monitoring Drift and Query Consistency
- Analyzing query drift in production settings
- Observing changes in schema and data
- Identifying irregularities in user queries
Recording Query Logs
- Capturing and archiving query history
- Utilizing historical data for audits and diagnostics
- Applying query insights to optimize performance
Observability and Monitoring Architectures
- Connecting with monitoring platforms and dashboards
- Defining metrics for reliability and precision
- Establishing alerting and incident management procedures
Enterprise Deployment Models
- Expanding observability practices across teams
- Balancing precision and performance in live systems
- Enforcing governance and accountability for AI-generated results
The Future of Quality and Observability in WrenAI
- AI-based self-correction capabilities
- Sophisticated evaluation frameworks
- Emerging features for enterprise observability
Conclusion and Future Directions
Requirements
- Knowledge of data quality and reliability standards
- Proficiency in SQL and analytical processes
- Exposure to monitoring or observability platforms
Target Audience
- Data reliability engineers
- BI managers
- QA specialists in analytics
