Course Outline

Introduction to WrenAI OSS

  • Overview of WrenAI architecture
  • Key OSS components and ecosystem
  • Installation and setup

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 queries
  • Improving SQL generation accuracy
  • Common challenges and troubleshooting

Prompt Tuning and Optimization

  • Prompt engineering strategies
  • Fine-tuning for enterprise datasets
  • Balancing accuracy and performance

Implementing Guardrails

  • Preventing unsafe or costly queries
  • Validation and approval mechanisms
  • Governance and compliance considerations

Integrating WrenAI into Data Workflows

  • Embedding Wren AI in 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 ML models
  • Scaling for large datasets

Summary and Next Steps

Requirements

  • Strong understanding of SQL and database systems
  • Experience with data modeling and semantic layers
  • Familiarity with machine learning or natural language processing concepts

Audience

  • Data engineers
  • Analytics engineers
  • ML engineers
 21 Hours

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