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.
Course Outline
Introduction to WrenAI OSS
- Overview of WrenAI architecture
- Key open-source components and ecosystem
- Installation and setup procedures
Semantic Modeling in WrenAI
- Defining semantic layers
- Designing reusable metrics and dimensions
- Best practices for consistency and maintainability
Text-to-SQL in Practice
- Mapping natural language inputs to SQL queries
- Improving accuracy in SQL generation
- Common challenges and troubleshooting techniques
Prompt Tuning and Optimization
- Prompt engineering strategies
- Fine-tuning for enterprise datasets
- Balancing accuracy with performance
Implementing Guardrails
- Preventing unsafe or costly queries
- Validation and approval mechanisms
- Governance and compliance considerations
Integrating WrenAI into Data Workflows
- Embedding WrenAI in data pipelines
- Connecting to BI and visualization tools
- Multi-user and enterprise deployments
Advanced Use Cases and Extensions
- Custom plugins and API integrations
- Extending WrenAI with machine learning models
- Scaling for large datasets
Summary and Next Steps
Requirements
- Proficient understanding of SQL and database systems.
- Prior experience with data modeling and semantic layers.
- Familiarity with machine learning or natural language processing concepts.
Target Audience
- Data engineers
- Analytics engineers
- Machine learning engineers
21 Hours
