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Duration 21 hours
Course Outline
Introduction to WrenAI OSS
- Overview of the WrenAI architectural design
- Core OSS components and the surrounding ecosystem
- System installation and initial configuration
Semantic Modeling within Wren AI
- Establishing semantic layers
- Creating reusable metrics and dimensions
- Best practices for maintaining consistency and ease of maintenance
Practical Application of Text to SQL
- Translating natural language into database queries
- Enhancing the accuracy of generated SQL
- Addressing common challenges and troubleshooting techniques
Prompt Tuning and Performance Optimisation
- Strategies for effective prompt engineering
- Fine-tuning models for enterprise-scale datasets
- Achieving a balance between accuracy and system performance
Deploying Guardrails
- Mitigating unsafe or resource-intensive queries
- Implementing validation and approval workflows
- Addressing governance and compliance requirements
Integrating WrenAI into Data Workflows
- Embedding Wren AI into data pipelines
- Linking with BI platforms and visualisation tools
- Managing multi-user and enterprise-level deployments
Advanced Applications and Extensions
- Developing custom plugins and API integrations
- Extending WrenAI capabilities with additional ML models
- Scaling operations for large datasets
Summary and Recommended Next Steps
Requirements
- A solid grasp of SQL and database management 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
