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
- Understanding the WrenAI architectural framework
- Exploring essential OSS components and the broader ecosystem
- Installation procedures and initial configuration
Building Semantic Models in Wren AI
- Creating effective semantic layers
- Developing reusable metrics and dimensions
- Best practices for maintaining consistency and ease of upkeep
Practical Text-to-SQL Implementation
- Translating natural language inputs into executable queries
- Strategies for boosting SQL generation precision
- Addressing common issues and debugging techniques
Refining Prompts and Performance
- Advanced prompt engineering techniques
- Fine-tuning models for enterprise-grade datasets
- Achieving the right balance between accuracy and speed
Deploying Safety Guardrails
- Mitigating risks from unsafe or resource-intensive queries
- Implementing validation checks and approval workflows
- Considering governance frameworks and compliance requirements
Embedding WrenAI in Data Operations
- Integrating Wren AI into automated pipelines
- Linking with BI platforms and visualization tools
- Managing multi-user access and enterprise-scale deployments
Advanced Scenarios and Expansion
- Developing custom plugins and API connections
- Augmenting WrenAI capabilities with additional ML models
- Scaling solutions for massive data volumes
Key Takeaways and Future Directions
Requirements
- Proficiency in SQL and database management systems
- Practical experience with data modeling and semantic layer design
- Basic knowledge of machine learning or natural language processing principles
Who Should Attend
- Data engineers
- Analytics engineers
- ML engineers
