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Duration 14 hours
Course Outline
Quality and Observability Fundamentals in WrenAI
- The significance of observability in AI-driven analytics.
- Key challenges in assessing NL to SQL outputs.
- Effective frameworks for quality monitoring.
Validating NL to SQL Precision
- Setting success benchmarks for generated queries.
- Establishing standard benchmarks and test datasets.
- Automating evaluation workflows.
Strategies for Prompt Tuning
- Refining prompts for optimal accuracy and efficiency.
- Adapting to specific domains through tuning.
- Managing prompt libraries for enterprise-scale applications.
Monitoring Drift and Query Consistency
- Recognizing query drift in production environments.
- Tracking schema and data evolution.
- Identifying anomalies in user-generated queries.
Recording Query Histories
- Logging and archiving query history.
- Utilizing historical data for audits and troubleshooting.
- Applying query insights to enhance performance.
Observability and Monitoring Ecosystems
- Connecting with monitoring tools and dashboards.
- Defining metrics for reliability and precision.
- Establishing alerting and incident response procedures.
Enterprise Deployment Models
- Extending observability across multiple teams.
- Striking a balance between accuracy and performance in live systems.
- Ensuring governance and accountability for AI-generated results.
The Future of Quality and Observability in WrenAI
- AI-powered self-correction capabilities.
- Next-generation evaluation frameworks.
- Planned features for enhanced enterprise observability.
Recap and Path Forward
Requirements
- A solid grasp of data quality and reliability standards
- Proficiency in SQL and analytics processes
- Knowledge of monitoring or observability platforms
Target Audience
- Data reliability engineers
- BI leaders
- QA specialists in analytics
