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Duration 21 hours
Course Outline
Introduction to WrenAI OSS
- Architectural overview of WrenAI
- Essential OSS components and the broader ecosystem
- Installation procedures and initial setup
Semantic Modeling with WrenAI
- Establishing semantic layers
- Creating reusable metrics and dimensions
- Best practices for ensuring consistency and ease of maintenance
Practical Text-to-SQL Application
- Translating natural language into SQL queries
- Strategies for enhancing SQL generation precision
- Addressing common challenges and troubleshooting issues
Prompt Tuning and Optimization
- Effective prompt engineering techniques
- Fine-tuning models for enterprise-scale datasets
- Achieving a balance between accuracy and processing performance
Guardrail Implementation
- Mitigating unsafe or resource-intensive queries
- Establishing validation and approval workflows
- Considering governance and compliance requirements
Integrating WrenAI into Data Workflows
- Incorporating WrenAI into data pipelines
- Connecting with BI and visualization platforms
- Managing multi-user and enterprise-level deployments
Advanced Use Cases and Extensions
- Developing custom plugins and API integrations
- Extending WrenAI capabilities with ML models
- Scaling solutions for large-scale datasets
Conclusion and Future Directions
Requirements
- Comprehensive understanding of SQL and database systems
- Practical experience with data modeling and semantic layers
- Familiarity with machine learning or natural language processing concepts
Target Audience
- Data engineers
- Analytics engineers
- ML engineers
