Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 21 hours
Course Outline
WrenAI OSS Fundamentals
- Structural overview of the WrenAI system
- Essential open-source components and the surrounding ecosystem
- Process for installation and initial configuration
Semantic Modeling with Wren AI
- Establishing semantic layer structures
- Creating metrics and dimensions that are easily reusable
- Strategies for maintaining consistency and ease of upkeep
Practical Application of Text to SQL
- Translating natural language inputs into database queries
- Techniques to boost the precision of generated SQL
- Addressing frequent obstacles and debugging methods
Prompt Refinement and Performance Enhancement
- Effective strategies for prompt engineering
- Adjusting models specifically for enterprise-scale datasets
- Achieving an optimal balance between result accuracy and system performance
Deployment of Safety Guardrails
- Methods to block dangerous or resource-intensive queries
- Establishing validation and approval protocols
- Considerations regarding governance and regulatory compliance
Integrating WrenAI into Data Operations
- Embedding Wren AI capabilities into automated pipelines
- Linking with business intelligence and visualization platforms
- Managing multi-user environments and enterprise-wide rollouts
Complex Scenarios and Advanced Extensions
- Developing custom plugins and establishing API connections
- Augmenting WrenAI capabilities with additional ML models
- Scaling solutions to handle vast data volumes
Conclusion and Future Recommendations
Requirements
- Profound knowledge of SQL and database architectures
- Hands-on experience with data modeling and semantic layer design
- Basic understanding of machine learning or natural language processing principles
Target Audience
- Data Engineers
- Analytics Engineers
- ML Engineers
