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Duration 21 hours
Course Outline
Introduction to WrenAI OSS
- Overview of WrenAI architecture
- Key OSS components and the surrounding ecosystem
- Installation and configuration procedures
Semantic Modelling in WrenAI
- Defining semantic layers
- Creating reusable metrics and dimensions
- Best practices for maintaining consistency and maintainability
Text-to-SQL in Practice
- Translating natural language into queries
- Enhancing the accuracy of SQL generation
- Addressing common challenges and troubleshooting issues
Prompt Tuning and Optimisation
- Strategies for prompt engineering
- Fine-tuning for enterprise-grade datasets
- Striking a balance between accuracy and performance
Implementing Guardrails
- Mitigating unsafe or costly queries
- Establishing validation and approval mechanisms
- Considering governance and compliance requirements
Integrating WrenAI into Data Workflows
- Embedding WrenAI into data pipelines
- Connecting to BI and visualisation tools
- Managing multi-user and enterprise deployments
Advanced Use Cases and Extensions
- Custom plugins and API integrations
- Extending WrenAI with machine learning models
- Scaling for large datasets
Summary and Next Steps
Requirements
- A solid grasp of SQL and database systems
- Practical experience with data modelling and semantic layers
- Knowledge of machine learning or natural language processing concepts
Target Audience
- Data engineers
- Analytics engineers
- ML engineers
