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Duration 21 hours
Course Outline
Introduction to WrenAI OSS
- Overview of WrenAI architecture
- Key OSS components and the broader ecosystem
- Installation and initial setup
Semantic Modeling in Wren AI
- Defining semantic layers
- Designing reusable metrics and dimensions
- Best practices for maintaining consistency and ease of upkeep
Text-to-SQL in Practice
- Mapping natural language to database queries
- Improving the precision of SQL generation
- Addressing common challenges and troubleshooting techniques
Prompt Tuning and Optimisation
- Prompt engineering strategies
- Fine-tuning for enterprise-grade datasets
- Balancing accuracy against performance
Implementing Guardrails
- Preventing unsafe or resource-intensive queries
- Establishing validation and approval mechanisms
- Governance and compliance considerations
Integrating WrenAI into Data Workflows
- Embedding Wren AI into data pipelines
- Connecting to BI and visualisation tools
- Multi-user and enterprise-level deployments
Advanced Use Cases and Extensions
- Custom plugins and API integrations
- Extending WrenAI with ML models
- Scaling for large datasets
Summary and Next Steps
Requirements
- A solid grasp of SQL and database systems
- Practical experience with data modeling and semantic layers
- Knowledge of machine learning or natural language processing principles
Target Audience
- Data engineers
- Analytics engineers
- ML engineers
