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Course Outline
Introduction to WrenAI OSS
- An overview of the WrenAI architecture
- Core OSS components and the broader ecosystem
- Steps for installation and initial setup
Semantic Modeling within Wren AI
- Creating and defining semantic layers
- Designing reusable metrics and dimensions
- Best practices for ensuring consistency and ease of maintenance
Text-to-SQL in Practice
- Translating natural language inputs into database queries
- Strategies for improving the accuracy of SQL generation
- Addressing common challenges and troubleshooting techniques
Prompt Tuning and Optimization
- Effective prompt engineering strategies
- Fine-tuning models for enterprise-scale datasets
- Balancing output accuracy with system performance
Implementing Guardrails
- Preventing unsafe or resource-intensive queries
- Establishing validation and approval mechanisms
- Considering governance and compliance requirements
Integrating WrenAI into Data Workflows
- Embedding Wren AI capabilities into data pipelines
- Connecting to BI dashboards and visualization tools
- Managing multi-user and enterprise-level deployments
Advanced Use Cases and Extensions
- Developing custom plugins and API integrations
- Expanding WrenAI capabilities with additional ML models
- Scaling solutions for large datasets
Summary and Next Steps
Requirements
- A solid grasp of SQL and database systems
- Practical experience with data modeling and semantic layers
- Familiarity with machine learning or natural language processing principles
Target Audience
- Data engineers
- Analytics engineers
- ML engineers
21 Hours
