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Duration 14 hours
Course Outline
Foundations of Quality and Observability in WrenAI
- The significance of observability in AI-driven analytics.
- Obstacles in assessing natural language to SQL conversions.
- Frameworks for overseeing quality.
Assessing NL to SQL Precision
- Defining success metrics for generated queries.
- Setting benchmarks and creating test datasets.
- Automating evaluation pipelines.
Prompt Refinement Strategies
- Optimizing prompts for both precision and efficiency.
- Adapting to specific domains through tuning.
- Managing prompt libraries for enterprise applications.
Monitoring Drift and Query Consistency
- Understanding query drift in production environments.
- Tracking schema and data changes.
- Identifying anomalies in user queries.
Instrumenting Query Histories
- Logging and archiving query history.
- Utilizing historical data for audits and issue resolution.
- Leveraging query insights for performance enhancements.
Monitoring and Observability Architectures
- Integration with monitoring tools and dashboards.
- Defining metrics for reliability and accuracy.
- Establishing alerting and incident response protocols.
Enterprise Deployment Models
- Scaling observability across multiple teams.
- Balancing accuracy with performance in live systems.
- Enforcing governance and accountability for AI outputs.
The Future of Quality and Observability in WrenAI
- AI-driven self-correction capabilities.
- Sophisticated evaluation frameworks.
- Emerging features for enterprise observability.
Recap and Subsequent Actions
Requirements
- Knowledge of data quality and integrity standards.
- Proficiency in SQL and analytical workflows.
- Familiarity with monitoring or observability platforms.
Target Audience
- Data reliability engineers.
- BI leads.
- QA specialists in analytics.
