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Course Outline
Introduction to WrenAI Open Source Software
- Overview of the WrenAI architecture
- Key open source components and ecosystem
- Installation and configuration procedures
Semantic Modeling in Wren AI
- Defining semantic layers for government data
- Designing reusable metrics and dimensions
- Best practices for consistency and maintainability
Text-to-SQL Implementation
- Mapping natural language to SQL queries
- Enhancing SQL generation accuracy
- Common challenges and troubleshooting
Prompt Tuning and Optimization
- Prompt engineering strategies for government applications
- Fine-tuning for enterprise datasets
- Balancing accuracy and performance
Implementing Guardrails
- Preventing unsafe or costly queries in government contexts
- Validation and approval mechanisms
- Governance and compliance considerations for government use
Integrating WrenAI into Data Workflows
- Embedding Wren AI in data pipelines
- Connecting to business intelligence and visualization tools
- Multi-user and enterprise deployments for government agencies
Advanced Use Cases and Extensions
- Custom plugins and API integrations for government systems
- Extending WrenAI with machine learning models
- Scaling for large datasets in government operations
Summary and Next Steps
Requirements
- Comprehensive proficiency in SQL and relational database architectures
- Proven capability in data modeling and semantic layer development
- Working knowledge of machine learning algorithms and natural language processing principles
Audience
- Data engineers
- Analytics engineers
- ML engineers for government initiatives
21 Hours