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Duration 21 hours
Course Outline
Getting Started with WrenAI OSS
- Overview of the WrenAI architectural framework.
- Exploring core OSS components and the surrounding ecosystem.
- Steps for installation and initial setup.
Semantic Modeling within Wren AI
- The process of defining semantic layers.
- Creating reusable metrics and dimensions.
- Best practices for maintaining consistency and ease of maintenance.
Practical Applications of Text to SQL
- Translating natural language inputs into executable queries.
- Strategies for boosting SQL generation precision.
- Addressing common obstacles and troubleshooting issues.
Prompt Tuning and Enhancement
- Effective prompt engineering techniques.
- Fine-tuning processes for enterprise-grade datasets.
- Achieving the right balance between accuracy and speed.
Establishing Guardrails
- Mitigating risks associated with unsafe or expensive queries.
- Implementing validation and approval workflows.
- Considering governance and compliance requirements.
Integrating WrenAI into Data Workflows
- Embedding Wren AI within data pipelines.
- Linking with BI platforms and visualization tools.
- Managing multi-user and enterprise-level deployments.
Advanced Scenarios and Extensions
- Developing custom plugins and API connections.
- Expanding WrenAI’s capabilities with additional ML models.
- Scaling solutions to handle large-scale datasets.
Recap and Future Directions
Requirements
- Proficient knowledge of SQL and database architectures.
- Practical experience in data modeling and managing semantic layers.
- Basic understanding of machine learning principles or natural language processing concepts.
Target Audience
- Data engineers.
- Analytics engineers.
- ML engineers.
