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Duration 14 hours
Course Outline
Introduction to Advanced Model Customization
- Overview of fine-tuning and prompt management capabilities in Vertex AI
- Practical use cases for model optimization
- Hands-on lab: configuring the Vertex AI workspace
Supervised Fine-Tuning of Gemini Models
- Preparing training datasets for fine-tuning
- Executing supervised fine-tuning pipelines
- Hands-on lab: fine-tuning a Gemini model
Prompt Engineering and Version Control
- Designing efficient prompts for generative AI
- Implementing version control for reproducibility
- Hands-on lab: developing and testing prompt versions
Evaluation and Benchmarking
- Introduction to evaluation libraries available in Vertex AI
- Automating testing and validation procedures
- Hands-on lab: assessing prompts and model outputs
Model Deployment and Monitoring
- Integrating optimized models into applications
- Tracking performance and detecting drift
- Hands-on lab: deploying a fine-tuned model
Best Practices for Enterprise AI Optimization
- Managing scalability and costs
- Addressing ethical considerations and mitigating bias
- Case study: enhancing AI applications in production
Future Directions in Fine-Tuning and Prompt Management
- Emerging trends in LLM optimization
- Automated prompt adaptation and reinforcement learning
- Strategic impact on enterprise adoption
Summary and Next Steps
Requirements
- Practical experience with machine learning workflows
- Proficiency in Python programming
- Working knowledge of cloud-based AI platforms
Target Audience
- AI engineers
- MLOps practitioners
- Data scientists
Testimonials (1)
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