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
Getting Started with WrenAI OSS
- High-level view of WrenAI architecture
- Essential OSS components and surrounding ecosystem
- Installation and initial configuration
Semantic Modeling within Wren AI
- Building semantic layers
- Creating reusable metrics and dimensions
- Best practices for ensuring consistency and ease of maintenance
Practical Text-to-SQL Applications
- Translating natural language into SQL queries
- Boosting the precision of generated SQL
- Addressing common issues and troubleshooting strategies
Prompt Refinement and Optimization
- Strategies for effective prompt engineering
- Adjusting models for enterprise-scale datasets
- Striking a balance between accuracy and processing speed
Establishing Safety Guardrails
- Blocking unsafe or resource-intensive queries
- Implementing validation and approval protocols
- Considerations for governance and regulatory compliance
Integrating WrenAI into Data Operations
- Embedding Wren AI within data pipelines
- Linking with BI platforms and visualization software
- Deployment strategies for multi-user and enterprise environments
Advanced Scenarios and Extensions
- Developing custom plugins and API connections
- Augmenting WrenAI with additional ML models
- Scaling solutions for large-scale datasets
Recap and Future Directions
Requirements
- Proficient knowledge of SQL and database architectures
- Practical experience with data modeling and semantic layers
- Understanding of machine learning or natural language processing principles
Target Audience
- Data engineers
- Analytics engineers
- ML engineers
