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Duration 21 hours
Course Outline
WrenAI OSS Introduction
- Overview of the WrenAI architecture
- Key open-source components and ecosystem tools
- Installation and configuration procedures
Semantic Modeling with Wren AI
- Defining semantic layers
- Creating reusable metrics and dimensions
- Best practices for ensuring consistency and maintainability
Practical Text-to-SQL Applications
- Translating natural language into database queries
- Improving the precision of SQL generation
- Addressing common challenges and troubleshooting strategies
Prompt Tuning and Optimization
- Strategies for prompt engineering
- Refining models for enterprise-scale datasets
- Achieving a balance between accuracy and system performance
Establishing Guardrails
- Preventing unsafe or resource-intensive queries
- Implementing validation and approval workflows
- Considerations for governance and regulatory compliance
Integrating WrenAI into Data Workflows
- Embedding Wren AI within data pipelines
- Connecting to business intelligence and visualization platforms
- Managing multi-user and enterprise-level deployments
Advanced Scenarios and Extensions
- Developing custom plugins and API integrations
- Extending WrenAI functionalities with machine learning models
- Scaling solutions for large-scale data environments
Recap and Future Steps
Requirements
- Proficiency in SQL and database management systems
- Hands-on experience with data modeling and semantic layers
- Knowledge of machine learning or natural language processing principles
Target Audience
- Data Engineers
- Analytics Engineers
- Machine Learning Engineers
