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Duration 21 hours
Course Outline
Getting to Know WrenAI OSS
- Understanding the WrenAI architectural framework
- Essential open-source components and ecosystem integration
- Setup and installation procedures
Semantic Modeling with Wren AI
- Building semantic layers
- Creating reusable metrics and dimensions
- Best practices for maintaining consistency and ease of maintenance
Practical Text-to-SQL Implementation
- Translating natural language inputs into database queries
- Elevating the precision of generated SQL
- Addressing common issues and troubleshooting strategies
Prompt Engineering and Optimization
- Strategies for effective prompt engineering
- Fine-tuning models for enterprise-scale datasets
- Striking a balance between accuracy and system performance
Deploying Guardrails
- Mitigating unsafe or resource-intensive queries
- Implementing validation and approval workflows
- Considering governance and compliance standards
Embedding WrenAI into Data Operations
- Integrating Wren AI into data pipelines
- Linking with BI platforms and visualization tools
- Managing multi-user and enterprise environments
Advanced Applications and Extensions
- Developing custom plugins and API connections
- Augmenting WrenAI capabilities with ML models
- Scaling solutions for extensive datasets
Wrap-up and Future Steps
Requirements
- Proficient knowledge of SQL and database management systems
- Practical experience with data modeling and semantic layers
- Familiarity with concepts in machine learning or natural language processing
Target Audience
- Data engineers
- Analytics engineers
- Machine learning engineers
