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 the WrenAI architecture.
- Key open-source source components and ecosystem.
- Installation and setup procedures.
Semantic Modeling in Wren AI
- Defining semantic layers.
- Designing reusable metrics and dimensions.
- Best practices for maintaining consistency and ease of maintenance.
Practical Application of Text to SQL
- Mapping natural language inputs to SQL queries.
- Strategies for improving SQL generation accuracy.
- Addressing common challenges and troubleshooting techniques.
Prompt Tuning and Optimization
- Strategies for effective prompt engineering.
- Fine-tuning processes for enterprise-level datasets.
- Balancing accuracy with performance efficiency.
Implementing Guardrails
- Preventing unsafe or costly queries.
- Establishing validation and approval mechanisms.
- Considering governance and compliance requirements.
Integrating WrenAI into Data Workflows
- Embedding Wren AI within data pipelines.
- Connecting to BI and visualization tools.
- Managing multi-user and enterprise deployments.
Advanced Use Cases and Extensions
- Developing custom plugins and API integrations.
- Extending WrenAI capabilities with ML models.
- Scaling solutions for large datasets.
Summary and Next Steps
Requirements
- A solid grasp of SQL and database systems.
- Prior experience with data modeling and semantic layers.
- Familiarity with concepts related to machine learning or natural language processing.
Target Audience
- Data engineers
- Analytics engineers
- ML engineers
21 Hours
