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Duration 21 hours
Course Outline
Getting Started with WrenAI OSS
- Architectural overview of WrenAI
- Core open-source components and ecosystem
- Installation and initial configuration
Semantic Modeling within Wren AI
- Construction of semantic layers
- Development of reusable metrics and dimensions
- Best practices for maintaining consistency and ease of maintenance
Practical Application of Text to SQL
- Translating natural language into database queries
- Enhancing the precision of SQL generation
- Addressing common issues and troubleshooting methods
Refinement and Optimization of Prompts
- Strategies for effective prompt engineering
- Tailoring models for enterprise-scale datasets
- Achieving a balance between accuracy and system performance
Deployment of Safety Controls
- Mitigation of unsafe or resource-intensive queries
- Implementation of validation and approval protocols
- Considerations for governance and regulatory compliance
Embedding WrenAI into Data Workflows
- Integration of Wren AI within data pipelines
- Linking with BI platforms and visualization tools
- Supporting multi-user and enterprise-level deployments
Complex Use Cases and Advanced Features
- Development of custom plugins and API connections
- Augmenting WrenAI capabilities with ML models
- Scaling solutions for large-volume datasets
Conclusion and Future Directions
Requirements
- Proficient knowledge of SQL and database architectures
- Practical experience with data modeling and semantic layers
- Working knowledge of machine learning or natural language processing principles
Target Audience
- Data engineers
- Analytics engineers
- ML engineers
