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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.
- Key use cases for optimizing model performance.
- Hands-on lab: Establishing the Vertex AI workspace.
Supervised Fine-Tuning of Gemini Models
- Preparing training datasets for fine-tuning processes.
- Executing supervised fine-tuning pipelines.
- Hands-on lab: Fine-tuning a Gemini model.
Prompt Engineering and Version Management
- Designing effective prompts for generative AI applications.
- Managing version control and ensuring reproducibility.
- Hands-on lab: Creating and testing distinct prompt versions.
Evaluation and Benchmarking
- Introduction to evaluation libraries available in Vertex AI.
- Automating testing and validation workflows.
- Hands-on lab: Evaluating prompts and model outputs.
Model Deployment and Monitoring
- Integrating optimized models into application architectures.
- Monitoring performance metrics and detecting drift.
- Hands-on lab: Deploying a fine-tuned model.
Best Practices for Enterprise AI Optimization
- Strategies for scalability and cost management.
- Ethical considerations and bias mitigation techniques.
- Case study: Enhancing AI applications in production settings.
Future Directions in Fine-Tuning and Prompt Management
- Emerging trends in LLM optimization.
- Automated prompt adaptation and reinforcement learning approaches.
- Strategic implications for enterprise adoption.
Summary and Next Steps
Requirements
- Practical experience with machine learning workflows.
- Proficiency in Python programming.
- Familiarity with cloud-based AI platforms.
Target Audience
- AI engineers
- MLOps practitioners
- Data scientists
Testimonials (1)
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