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Course Outline
Introduction to WrenAI OSS for Government
- Overview of the WrenAI architecture
- Key open-source components and ecosystem
- Installation and setup procedures
Semantic Modeling in Wren AI for Government
- Defining semantic layers to enhance data interpretation
- Designing reusable metrics and dimensions for consistent reporting
- Best practices for maintaining consistency and ensuring long-term maintainability
Text to SQL in Practice for Government
- Mapping natural language queries to structured SQL commands
- Strategies for improving the accuracy of generated SQL
- Common challenges and methods for effective troubleshooting
Prompt Tuning and Optimization for Government
- Advanced prompt engineering techniques
- Fine-tuning models to optimize performance on government datasets
- Balancing accuracy with computational efficiency
Implementing Guardrails for Government
- Measures to prevent unsafe or resource-intensive queries
- Implementation of validation and approval processes
- Governance and compliance considerations specific to government operations
Integrating WrenAI into Data Workflows for Government
- Embedding Wren AI in data processing pipelines
- Connecting with business intelligence and visualization tools
- Strategies for multi-user and enterprise-level deployments within government agencies
Advanced Use Cases and Extensions for Government
- Developing custom plugins and API integrations to meet specific needs
- Extending WrenAI capabilities with machine learning models
- Scaling the solution for handling large government datasets
Summary and Next Steps for Government
Requirements
- A strong understanding of SQL and database systems for government applications.
- Experience with data modeling and semantic layers to support public sector workflows.
- Familiarity with machine learning or natural language processing concepts to enhance governance and accountability.
Audience
- Data engineers for government
- Analytics engineers for government
- ML engineers for government
21 Hours