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Duration 21 hours
Course Outline
Introduction to WrenAI OSS
- Overview of the WrenAI architecture
- Key open-source components and ecosystem
- Installation and configuration
Semantic Modeling in Wren AI
- Defining semantic layers
- Designing reusable metrics and dimensions
- Best practices for maintaining consistency and maintainability
Text to SQL in Practice
- Mapping natural language to database queries
- Improving the accuracy of SQL generation
- Addressing common challenges and troubleshooting issues
Prompt Tuning and Optimization
- Prompt engineering strategies
- Fine-tuning for enterprise datasets
- Balancing accuracy against performance
Implementing Guardrails
- Preventing unsafe or expensive queries
- Validation and approval workflows
- Governance and compliance considerations
Integrating WrenAI into Data Workflows
- Embedding Wren AI within data pipelines
- Connecting to BI and visualization tools
- Multi-user and enterprise deployment strategies
Advanced Use Cases and Extensions
- Custom plugins and API integrations
- Extending WrenAI with machine learning models
- Scaling for large datasets
Summary and Next Steps
Requirements
- Proficiency in SQL and database systems
- Hands-on experience with data modeling and semantic layers
- Knowledge of machine learning or natural language processing concepts
Target Audience
- Data engineers
- Analytics engineers
- ML engineers
