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Course Outline
Introduction to Advanced Model Customization
- Overview of fine-tuning and prompt governance in Vertex AI
- Key applications for model optimization
- Practical exercise: initializing the Vertex AI workspace
Supervised Fine-Tuning for Gemini Models
- Curating training datasets for fine-tuning
- Executing supervised fine-tuning pipelines
- Practical exercise: fine-tuning a Gemini instance
Prompt Design and Version Control
- Crafting high-impact prompts for generative AI
- Managing version control and ensuring reproducibility
- Practical exercise: developing and validating prompt iterations
Assessment and Benchmarking
- Introduction to Vertex AI evaluation libraries
- Automating testing and verification processes
- Practical exercise: assessing prompts and resulting outputs
Model Release and Oversight
- Incorporating optimized models into application frameworks
- Tracking performance metrics and detecting drift
- Practical exercise: releasing a fine-tuned model
Best Practices for Enterprise AI Enhancement
- Managing scalability and operational costs
- Addressing ethical considerations and bias mitigation
- Case study: enhancing production AI applications
Future Trends in Fine-Tuning and Prompt Governance
- Emerging trends in LLM optimization
- Automated prompt adaptation and reinforcement learning approaches
- Strategic impact on enterprise adoption
Conclusion and Path Forward
Requirements
- Proficiency in machine learning pipelines
- Proficiency in Python development
- Understanding of cloud-native AI infrastructure
Target Audience
- AI Engineers
- MLOps Specialists
- Data Scientists
14 Hours
Testimonials (1)
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