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Duration 21 hours
Course Outline
Introduction to WrenAI OSS
- High-level view of WrenAI architecture
- Essential OSS components and ecosystem elements
- Initial installation and configuration
Semantic Modeling with Wren AI
- Establishing semantic layers
- Creating reusable metrics and dimensions
- Best practices for ensuring consistency and ease of maintenance
Applied Text-to-SQL Techniques
- Translating natural language into database queries
- Enhancing the precision of SQL generation
- Addressing common challenges and diagnostic strategies
Prompt Tuning and Performance Optimization
- Strategies for effective prompt engineering
- Fine-tuning models for enterprise-scale datasets
- Achieving a balance between accuracy and system performance
Guardrail Implementation
- Mitigating unsafe or resource-intensive queries
- Setting up validation and approval protocols
- Considerations for governance and regulatory compliance
WrenAI Integration in Data Workflows
- Embedding Wren AI within existing pipelines
- Linking with BI platforms and visualization tools
- Deployments for multi-user and enterprise environments
Advanced Applications and Extensions
- Developing custom plugins and API connections
- Expanding WrenAI capabilities with additional ML models
- Scaling solutions for extensive datasets
Recap and Future Directions
Requirements
- A solid grasp of SQL and database architectures
- Prior experience with data modeling and semantic layers
- Knowledge of machine learning or natural language processing principles
Target Audience
- Data engineers
- Analytics engineers
- ML engineers
