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Course Outline
Introduction to WrenAI OSS
- Overview of the WrenAI architecture.
- Key open-source components and ecosystem.
- Installation and setup processes.
Semantic Modeling in Wren AI
- Defining semantic layers.
- Designing reusable metrics and dimensions.
- Best practices for ensuring consistency and maintainability.
Practical Text-to-SQL Conversion
- Mapping natural language inputs to SQL queries.
- Strategies to improve SQL generation accuracy.
- Common challenges and troubleshooting techniques.
Prompt Tuning and Optimization
- Prompt engineering strategies.
- Fine-tuning for enterprise datasets.
- Balancing accuracy with performance.
Implementing Guardrails
- Preventing unsafe or costly queries.
- Validation and approval mechanisms.
- Governance and compliance considerations.
Integrating WrenAI into Data Workflows
- Embedding Wren AI within pipelines.
- Connecting to BI and visualization tools.
- Managing multi-user and enterprise deployments.
Advanced Use Cases and Extensions
- Creating custom plugins and API integrations.
- Extending WrenAI with machine learning models.
- Scaling solutions for large datasets.
Summary and Next Steps
Requirements
- Solid understanding of SQL and database systems.
- Experience in data modeling and working with semantic layers.
- Familiarity with machine learning or natural language processing concepts.
Target Audience
- Data engineers.
- Analytics engineers.
- ML engineers.
21 Hours
