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Duration 21 hours
Course Outline
Fundamentals of WrenAI OSS
- Architectural overview of WrenAI
- Core open-source elements and the surrounding ecosystem
- Setup and installation procedures
Applying Semantic Modeling in Wren AI
- Creation of semantic layers
- Formulating reusable metrics and dimensions
- Recommended methods for ensuring consistency and ease of maintenance
Practical Application of Text to SQL
- Translating natural language into database queries
- Boosting the precision of SQL generation
- Addressing typical issues and debugging strategies
Refining and Optimizing Prompts
- Strategies for effective prompt engineering
- Adjusting models for enterprise-level datasets
- Maintaining a balance between accuracy and system performance
Deployment of Safety Guardrails
- Avoiding unsafe or resource-intensive queries
- Establishing validation and approval workflows
- Addressing governance and regulatory compliance
Embedding WrenAI in Data Processes
- Integrating Wren AI into data pipelines
- Linking with business intelligence and visualization platforms
- Deployments for multi-user and enterprise environments
Complex Scenarios and Extensions
- Developing custom plugins and API connections
- Expanding WrenAI capabilities through ML models
- Managing scalability for massive datasets
Recap and Future Directions
Requirements
- Solid grasp of SQL and database architectures
- Background in data modeling and semantic layer construction
- Awareness of machine learning or natural language processing principles
Target Audience
- Data engineers
- Analytics engineers
- ML engineers
