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 Duration 21 hours

Course Outline

Introduction to WrenAI OSS

  • Architectural overview of WrenAI
  • Primary open-source components and ecosystem
  • Installation and configuration procedures

Semantic Modeling in Wren AI

  • Definition of semantic layers
  • Design of reusable metrics and dimensions
  • Best practices for consistency and maintenance

Practical Text to SQL Implementation

  • Mapping natural language to database queries
  • Enhancing the accuracy of SQL generation
  • Addressing common challenges and troubleshooting issues

Prompt Tuning and Optimization

  • Strategies for prompt engineering
  • Fine-tuning for enterprise datasets
  • Balancing output accuracy with system performance

Implementing Safeguards

  • Prevention of unsafe or costly queries
  • Establishment of validation and approval mechanisms
  • Governance and compliance considerations

Integrating WrenAI into Data Workflows

  • Embedding Wren AI in data pipelines
  • Connection to business intelligence and visualization tools
  • Multi-user and enterprise-level deployments

Advanced Applications and Extensions

  • Development of custom plugins and API integrations
  • Extending WrenAI with machine learning models
  • Scaling for large datasets

Summary and Subsequent Actions

Requirements

  • Comprehensive understanding of SQL and database systems
  • Professional experience with data modeling and semantic layers
  • Proficiency with machine learning or natural language processing concepts

Target Audience

  • Data engineers
  • Analytics engineers
  • Machine learning engineers

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