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Duration 21 hours
Course Outline
Introduction to WrenAI OSS
- Overview of WrenAI architecture.
- Key OSS components and ecosystem.
- Installation and setup procedures.
Semantic Modeling in Wren AI
- Defining semantic layers.
- Designing reusable metrics and dimensions.
- Best practices for consistency and maintainability.
Text to SQL in Practice
- Mapping natural language to SQL queries.
- Improving the accuracy of SQL generation.
- Addressing common challenges and troubleshooting.
Prompt Tuning and Optimization
- Prompt engineering strategies.
- Fine-tuning models for enterprise datasets.
- Balancing accuracy with performance.
Implementing Guardrails
- Preventing unsafe or high-cost queries.
- Establishing validation and approval mechanisms.
- Considerations for governance and compliance.
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
- Proficiency in SQL and database systems.
- Experience with data modeling and semantic layers.
- Familiarity with machine learning or natural language processing principles.
Target Audience
- Data engineers.
- Analytics engineers.
- ML engineers.
