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Duration 21 hours
Course Outline
Overview of WrenAI OSS
- Architectural insights into WrenAI
- Essential OSS components and surrounding ecosystem
- Initial installation and configuration steps
Semantic Modeling with Wren AI
- Creating effective semantic layers
- Structuring reusable metrics and dimensions
- Best practices for ensuring consistency and ease of maintenance
Practical Text-to-SQL Implementation
- Translating natural language inputs into SQL queries
- Techniques for boosting the precision of generated SQL
- Addressing frequent issues and debugging strategies
Refining Prompts and Optimization
- Effective prompt engineering methodologies
- Fine-tuning models for enterprise-grade datasets
- Achieving the right balance between accuracy and speed
Establishing Guardrails
- Mitigating risks from unsafe or expensive queries
- Implementing validation and approval protocols
- Considering governance and compliance requirements
WrenAI Integration into Data Workflows
- Embedding Wren AI into existing data pipelines
- Linking with Business Intelligence and visualization platforms
- Managing multi-user and enterprise-scale deployments
Advanced Scenarios and Extensions
- Developing custom plugins and API connections
- Augmenting WrenAI capabilities with additional ML models
- Scaling solutions for massive datasets
Recap and Future Directions
Requirements
- Profound knowledge of SQL and database architectures
- Hands-on experience with data modeling and semantic layer concepts
- Basic familiarity with machine learning or natural language processing principles
Target Audience
- Data engineers
- Analytics engineers
- ML engineers
