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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 exercise: configuring the Vertex AI workspace
Supervised Fine-Tuning of Gemini Models
- Curating training datasets for fine-tuning
- Executing supervised fine-tuning pipelines
- Hands-on exercise: fine-tuning a Gemini model
Prompt Engineering and Version Control
- Crafting effective prompts for generative AI systems
- Managing versions to ensure reproducibility
- Hands-on exercise: creating and testing different prompt versions
Evaluation and Benchmarking
- Exploring evaluation libraries available in Vertex AI
- Automating testing and validation processes
- Hands-on exercise: assessing prompts and model outputs
Model Deployment and Monitoring
- Integrating refined models into application stacks
- Tracking performance metrics and detecting drift
- Hands-on exercise: deploying a fine-tuned model
Best Practices for Enterprise AI Optimization
- Managing scalability and cost efficiency
- Addressing ethical considerations and mitigating bias
- Case study: enhancing AI application performance in production
Future Directions in Fine-Tuning and Prompt Management
- Evolving trends in LLM optimization
- Automated prompt adaptation and reinforcement learning techniques
- Strategic impact on enterprise adoption
Summary and Next Steps
Requirements
- Practical experience with machine learning workflows
- Proficiency in Python programming
- Familiarity with cloud-native AI platforms
Target Audience
- AI Engineers
- MLOps Specialists
- Data Scientists
Testimonials (1)
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