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Course Outline
Getting Started with WrenAI OSS
- An overview of the WrenAI architecture
- Core open-source components and the surrounding ecosystem
- Installation procedures and initial setup
Semantic Modeling with Wren AI
- Constructing semantic layers
- Creating reusable metrics and dimensions
- Best practices for ensuring consistency and ease of maintenance
Applying Text-to-SQL in Real Scenarios
- Translating natural language inputs into database queries
- Strategies to boost SQL generation precision
- Addressing common pitfalls and troubleshooting methods
Refining Prompts and Performance
- Effective prompt engineering techniques
- Fine-tuning models for enterprise-scale datasets
- Achieving an optimal balance between accuracy and speed
Deploying Safety Guardrails
- Shielding against unsafe or resource-intensive queries
- Establishing validation and approval protocols
- Considering governance and compliance requirements
Embedding WrenAI in Data Operations
- Integrating Wren AI into data pipelines
- Linking with BI platforms and visualization tools
- Managing multi-user and enterprise-level deployments
Advanced Applications and Extensions
- Developing custom plugins and API connections
- Augmenting WrenAI capabilities with ML models
- Scaling solutions for extensive datasets
Recap and Future Directions
Requirements
- A solid grasp of SQL and database architectures
- Practical 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
21 Hours