Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to WrenAI OSS
- Overview of WrenAI architecture.
- Key open-source components and ecosystem.
- Installation and setup procedures.
Semantic Modeling in WrenAI
- Defining semantic layers.
- Designing reusable metrics and dimensions.
- Best practices for consistency and maintainability.
Text-to-SQL in Practice
- Mapping natural language to SQL queries.
- Improving the accuracy of SQL generation.
- Addressing common challenges and troubleshooting.
Prompt Tuning and Optimization
- Prompt engineering strategies.
- Fine-tuning for enterprise datasets.
- Balancing accuracy and performance.
Implementing Guardrails
- Preventing 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 visualization 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 strong understanding of SQL and database systems.
- Experience with data modeling and semantic layers.
- Familiarity with machine learning or natural language processing concepts.
Audience
- Data engineers
- Analytics engineers
- Machine learning engineers
21 Hours
