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Duration 21 hours
Course Outline
Overview of WrenAI OSS
- Understanding the WrenAI architectural design
- Core open-source components and the broader ecosystem
- Installation procedures and initial configuration
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 SQL queries
- Enhancing the precision of generated SQL
- Addressing common issues and debugging techniques
Prompt Refinement and Optimization
- Strategies for effective prompt engineering
- Adjusting models for enterprise-scale datasets
- Achieving a balance between result accuracy and system performance
Establishing Safety Guardrails
- Blocking unsafe or resource-intensive queries
- Implementing validation and approval protocols
- Addressing governance and compliance requirements
Embedding WrenAI in Data Operations
- Integrating Wren AI into data pipelines
- Linking with BI platforms and visualization software
- Managing multi-user and enterprise-level deployments
Complex Scenarios and Expansion
- Developing custom plugins and API connections
- Augmenting WrenAI with additional ML models
- Scaling solutions for large-scale datasets
Conclusion and Future Directions
Requirements
- Proficient knowledge of SQL and database architectures
- Prior experience with data modeling and semantic layer frameworks
- Understanding of machine learning or natural language processing principles
Target Participants
- Data engineers
- Analytics engineers
- Machine Learning engineers
