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Duration 14 hours
Course Outline
Introduction to Advanced Model Customization
- Overview of fine-tuning and prompt management features in Vertex AI
- Key use cases for model optimization
- Practical exercise: Configuring the Vertex AI workspace
Supervised Fine-Tuning of Gemini Models
- Preparing datasets for fine-tuning
- Executing supervised fine-tuning pipelines
- Practical exercise: Fine-tuning a Gemini model
Prompt Engineering and Version Management
- Structuring effective prompts for generative AI
- Managing version control and ensuring reproducibility
- Practical exercise: Developing and testing prompt versions
Evaluation and Benchmarking
- Review of evaluation libraries available in Vertex AI
- Streamlining testing and validation processes
- Practical exercise: Assessing prompts and model outputs
Model Deployment and Monitoring
- Integrating optimized models into applications
- Tracking performance and detecting drift
- Practical exercise: Deploying a fine-tuned model
Best Practices for Enterprise AI Optimization
- Managing scalability and cost efficiency
- Addressing ethical considerations and bias reduction
- 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
- Familiarity with cloud-based AI platforms
Target Audience
- AI Engineers
- MLOps Practitioners
- Data Scientists
Testimonials (1)
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