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Duration 21 hours
Course Outline
Introduction to WrenAI OSS
- Overview of the WrenAI architecture
- Key OSS components and the surrounding ecosystem
- Installation and initial setup
Semantic Modeling in Wren AI
- Defining semantic layers
- Designing reusable metrics and dimensions
- Best practices for ensuring consistency and maintainability
Text-to-SQL in Practice
- Mapping natural language to SQL queries
- Improving the accuracy of SQL generation
- Addressing common challenges and troubleshooting issues
Prompt Tuning and Optimization
- Strategies for prompt engineering
- Fine-tuning models for enterprise datasets
- Balancing accuracy with performance
Implementing Guardrails
- Preventing unsafe or high-cost queries
- Establishing validation and approval mechanisms
- Considering governance and compliance requirements
Integrating WrenAI into Data Workflows
- Embedding Wren AI within data pipelines
- Connecting to BI and visualization tools
- Deploying for multi-user and enterprise environments
Advanced Use Cases and Extensions
- Creating custom plugins and API integrations
- Extending WrenAI capabilities with ML models
- Scaling solutions for large datasets
Summary and Next Steps
Requirements
- A solid understanding of SQL and database systems
- Practical experience with data modeling and semantic layers
- Familiarity with machine learning or natural language processing concepts
Target Audience
- Data engineers
- Analytics engineers
- ML engineers
