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Duration 21 hours
Course Outline
Introduction to WrenAI OSS
- Overview of the WrenAI architecture
- Key OSS components and the surrounding ecosystem
- Installation and initial setup
Semantic Modeling with Wren AI
- Defining semantic layers
- Creating reusable metrics and dimensions
- Best practices for ensuring consistency and maintainability
Practical Text-to-SQL
- Translating natural language into queries
- Boosting the accuracy of SQL generation
- Addressing common challenges and troubleshooting
Prompt Tuning and Optimization
- Strategies for prompt engineering
- Fine-tuning for enterprise-level datasets
- Striking a balance between accuracy and performance
Implementing Guardrails
- Mitigating unsafe or expensive queries
- Establishing validation and approval mechanisms
- Considerations for governance and compliance
Integrating WrenAI into Data Workflows
- Incorporating Wren AI into pipelines
- Linking with BI and visualization tools
- Multi-user and enterprise deployment strategies
Advanced Use Cases and Extensions
- Custom plugins and API integrations
- Extending WrenAI with ML models
- Scaling for large-scale datasets
Summary and Next Steps
Requirements
- Solid grasp of SQL and database systems
- Practical experience with data modeling and semantic layers
- Knowledge of machine learning or natural language processing concepts
Target Audience
- Data engineers
- Analytics engineers
- ML engineers
