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Duration 21 hours
Course Outline
Introduction to WrenAI OSS
- An overview of the WrenAI architecture
- Essential OSS components and the surrounding ecosystem
- Installation and initial setup
Semantic Modeling with Wren AI
- Establishing semantic layers
- Creating reusable metrics and dimensions
- Best practices for ensuring consistency and ease of maintenance
Text-to-SQL in Action
- Translating natural language into SQL queries
- Boosting the accuracy of generated SQL
- Addressing common challenges and troubleshooting issues
Prompt Tuning and Optimization
- Strategies for effective prompt engineering
- Fine-tuning models for enterprise-level datasets
- Achieving a balance between accuracy and system performance
Applying Guardrails
- Mitigating unsafe or resource-intensive queries
- Establishing validation and approval workflows
- Considering governance and compliance requirements
Integrating WrenAI into Data Workflows
- Embedding Wren AI into existing data pipelines
- Linking with BI and visualization platforms
- Deploying for multi-user and enterprise environments
Advanced Use Cases and Extensions
- Developing custom plugins and API integrations
- Expanding WrenAI capabilities with ML models
- Scaling solutions for large-scale datasets
Recap and Future Directions
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 concepts
Target Audience
- Data engineers
- Analytics engineers
- ML engineers
