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Schița de curs
Introduction to WrenAI OSS
- Overview of WrenAI architecture
- Key OSS components and ecosystem
- Installation and setup
Semantic Modeling in Wren AI
- Defining semantic layers
- Designing reusable metrics and dimensions
- Best practices for consistency and maintainability
Text to SQL in Practice
- Mapping natural language to queries
- Improving SQL generation accuracy
- Common challenges and troubleshooting
Prompt Tuning and Optimization
- Prompt engineering strategies
- Fine-tuning for enterprise datasets
- Balancing accuracy and performance
Implementing Guardrails
- Preventing unsafe or costly queries
- Validation and approval mechanisms
- Governance and compliance considerations
Integrating WrenAI into Data Workflows
- Embedding Wren AI in pipelines
- Connecting to BI and visualization tools
- Multi-user and enterprise deployments
Advanced Use Cases and Extensions
- Custom plugins and API integrations
- Extending WrenAI with ML models
- Scaling for large datasets
Summary and Next Steps
Cerințe
- Compreensiune puternică a limbajului SQL și sistemelor de baze de date
- Experiență în modelarea datelor și straturi semantice
- Cunoștințe despre conceptele de învățare automată sau procesare a limbajului natural
Publicul-țintă
- Inginerii de date
- Inginerii de analize
- Inginerii de IA
21 ore
