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Course Outline
Introduction to Advanced Model Customization
- Overview of fine-tuning and prompt management in Vertex AI
- Use cases for model optimization
- Hands-on lab: setting up the Vertex AI workspace
Supervised Fine-Tuning of Gemini Models
- Preparing training data for fine-tuning
- Running supervised fine-tuning pipelines
- Hands-on lab: fine-tuning a Gemini model
Prompt Engineering and Version Management
- Designing effective prompts for generative AI
- Version control and reproducibility
- Hands-on lab: creating and testing prompt versions
Evaluation and Benchmarking
- Overview of evaluation libraries in Vertex AI
- Automating testing and validation workflows
- Hands-on lab: evaluating prompts and outputs
Model Deployment and Monitoring
- Integrating optimized models into applications
- Monitoring performance and drift detection
- Hands-on lab: deploying a fine-tuned model
Best Practices for Enterprise AI Optimization
- Scalability and cost management
- Ethical considerations and bias mitigation
- Case study: improving AI applications in production
Future Directions in Fine-Tuning and Prompt Management
- Emerging trends in LLM optimization
- Automated prompt adaptation and reinforcement learning
- Strategic implications for enterprise adoption
Summary and Next Steps
Requirements
- Experience with machine learning workflows
- Knowledge of Python programming
- Familiarity with cloud-based AI platforms
Audience
- AI engineers
- MLops practitioners
- Data scientists
14 Hours
Testimonials (1)
Overall, the training was very informative, the trainer provided different use case scenario and exercises so we can be familiarized with the Vertex AI application.
