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Duration 21 hours
Course Outline
Introduction to WrenAI OSS
- Architectural overview of WrenAI
- Primary open-source components and ecosystem
- Installation and configuration procedures
Semantic Modeling in Wren AI
- Definition of semantic layers
- Design of reusable metrics and dimensions
- Best practices for consistency and maintenance
Practical Text to SQL Implementation
- Mapping natural language to database queries
- Enhancing the accuracy of SQL generation
- Addressing common challenges and troubleshooting issues
Prompt Tuning and Optimization
- Strategies for prompt engineering
- Fine-tuning for enterprise datasets
- Balancing output accuracy with system performance
Implementing Safeguards
- Prevention of unsafe or costly queries
- Establishment of validation and approval mechanisms
- Governance and compliance considerations
Integrating WrenAI into Data Workflows
- Embedding Wren AI in data pipelines
- Connection to business intelligence and visualization tools
- Multi-user and enterprise-level deployments
Advanced Applications and Extensions
- Development of custom plugins and API integrations
- Extending WrenAI with machine learning models
- Scaling for large datasets
Summary and Subsequent Actions
Requirements
- Comprehensive understanding of SQL and database systems
- Professional experience with data modeling and semantic layers
- Proficiency with machine learning or natural language processing concepts
Target Audience
- Data engineers
- Analytics engineers
- Machine learning engineers