Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Quality and Observability in WrenAI
- The importance of observability in AI-driven analytics.
- Challenges associated with evaluating Natural Language to SQL conversions.
- Frameworks for monitoring quality.
Evaluating NL to SQL Accuracy
- Defining success criteria for generated queries.
- Establishing benchmarks and test datasets.
- Automating evaluation pipelines.
Prompt Tuning Techniques
- Optimizing prompts for improved accuracy and efficiency.
- Adapting to specific domains through tuning.
- Managing prompt libraries for enterprise usage.
Tracking Drift and Query Reliability
- Understanding query drift in production environments.
- Monitoring the evolution of schemas and data.
- Detecting anomalies in user queries.
Instrumenting Query History
- Logging and storing query history.
- Utilizing history for audits and troubleshooting.
- Leveraging query insights for performance enhancements.
Monitoring and Observability Frameworks
- Integrating with monitoring tools and dashboards.
- Key metrics for reliability and accuracy.
- Alerting and incident response procedures.
Enterprise Implementation Patterns
- Scaling observability across multiple teams.
- Balancing accuracy and performance in production.
- Governance and accountability for AI outputs.
Future of Quality and Observability in WrenAI
- AI-driven self-correction mechanisms.
- Advanced evaluation frameworks.
- Upcoming features for enterprise observability.
Summary and Next Steps
Requirements
- Knowledge of data quality and reliability practices.
- Experience with SQL and analytics workflows.
- Familiarity with monitoring or observability tools.
Audience
- Data reliability engineers.
- Business Intelligence (BI) leads.
- QA professionals specializing in analytics.
14 Hours
