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Course Outline
Introduction to Advanced Model Customization
- Overview of fine-tuning and prompt management capabilities in Vertex AI
- Identifying use cases for model optimization
- Practical exercise: Configuring the Vertex AI workspace
Supervised Fine-Tuning of Gemini Models
- Preparing training datasets for fine-tuning processes
- Executing supervised fine-tuning pipelines
- Practical exercise: Fine-tuning a Gemini model
Prompt Engineering and Version Control
- Crafting effective prompts for generative AI applications
- Managing version control to ensure reproducibility
- Practical exercise: Developing and testing prompt iterations
Evaluation and Benchmarking Strategies
- Exploring evaluation libraries available in Vertex AI
- Automating testing and validation workflows
- Practical exercise: Assessing prompt quality and model outputs
Model Deployment and Monitoring
- Integrating optimized models into application stacks
- Monitoring performance metrics and detecting drift
- Practical exercise: Deploying a fine-tuned model
Enterprise Best Practices for AI Optimization
- Managing scalability and resource costs
- Addressing ethical considerations and mitigating bias
- Case study: Enhancing AI application performance in production
Future Directions in Fine-Tuning and Prompt Management
- Exploring emerging trends in LLM optimization
- Understanding automated prompt adaptation and reinforcement learning
- Strategic implications for 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
14 Hours
Testimonials (1)
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