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Duration 21 hours
Course Outline
Getting Started with WrenAI OSS
- Overview of the WrenAI architecture
- Key OSS components and surrounding ecosystem
- Installation procedures and initial configuration
Semantic Modeling with Wren AI
- Establishing semantic layers
- Creating reusable metrics and dimensions
- Best practices for maintaining consistency and ease of maintenance
Practical Text-to-SQL Applications
- Translating natural language into database queries
- Boosting the precision of SQL generation
- Addressing common pitfalls and troubleshooting strategies
Prompt Engineering and Optimization
- Strategies for effective prompt engineering
- Fine-tuning models for enterprise-scale datasets
- Achieving a balance between accuracy and system performance
Deploying Guardrails
- Safeguarding against unsafe or resource-intensive queries
- Implementing validation and approval protocols
- Considerations for governance and regulatory compliance
Embedding WrenAI into Data Workflows
- Integrating Wren AI within data pipelines
- Linking with BI platforms and visualization tools
- Managing multi-user and enterprise-level deployments
Advanced Scenarios and Expansions
- Developing custom plugins and API connections
- Augmenting WrenAI with additional ML models
- Scaling solutions for extensive datasets
Recap and Future Actions
Requirements
- Proficient command of SQL and database systems
- Background in data modeling and semantic layers
- Familiarity with machine learning or natural language processing principles
Target Audience
- Data engineers
- Analytics engineers
- ML engineers
