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Duration 21 hours
Course Outline
Introduction to WrenAI OSS
- Architectural overview of WrenAI
- Key open-source components and the broader ecosystem
- Initial installation and configuration steps
Semantic Modeling with Wren AI
- Establishing semantic layers
- Creating reusable metrics and dimensions
- Best practices for ensuring consistency and ease of maintenance
Practical Application of Text to SQL
- Translating natural language into database queries
- Strategies to enhance the precision of SQL generation
- Addressing common challenges and debugging issues
Prompt Tuning and Performance Optimization
- Effective prompt engineering techniques
- Adjusting parameters for enterprise-grade datasets
- Achieving a balance between output accuracy and system performance
Deploying Safety Controls
- Mitigating risks from unsafe or resource-intensive queries
- Implementing validation and approval protocols
- Considerations for governance and regulatory compliance
Incorporating WrenAI into Data Workflows
- Integrating Wren AI into existing data pipelines
- Linking with BI dashboards and visualization platforms
- Managing multi-user environments and enterprise-scale deployments
Advanced Scenarios and Extensions
- Developing custom plugins and API connections
- Augmenting WrenAI capabilities with machine learning models
- Scaling operations for large-volume datasets
Summary and Recommended Next Steps
Requirements
- Profound knowledge of SQL and database architectures
- Practical experience with data modeling and semantic layers
- Familiarity with machine learning or natural language processing principles
Target Audience
- Data engineers
- Analytics engineers
- ML engineers
