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Course Outline
Introduction to Advanced Model Customization
- Foundations of fine-tuning and prompt management in Vertex AI
- Key use cases for model optimization
- Practical lab: initializing the Vertex AI workspace
Supervised Fine-Tuning of Gemini Models
- Preparing and structuring training data for fine-tuning
- Executing supervised fine-tuning pipelines
- Practical lab: fine-tuning a Gemini model
Prompt Engineering and Version Management
- Crafting effective prompts for generative AI tasks
- Managing version control and ensuring reproducibility
- Practical lab: developing and testing prompt versions
Evaluation and Benchmarking
- Exploring evaluation libraries available in Vertex AI
- Automating testing and validation procedures
- Practical lab: assessing prompts and model outputs
Model Deployment and Monitoring
- Integrating optimized models into application architectures
- Tracking performance metrics and detecting data drift
- Practical lab: deploying a fine-tuned model
Best Practices for Enterprise AI Optimization
- Managing scalability and operational costs
- Addressing ethical considerations and mitigating bias
- Case study: enhancing AI application performance in production
Future Directions in Fine-Tuning and Prompt Management
- Emerging trends in LLM optimization
- Techniques for automated prompt adaptation and reinforcement learning
- Strategic implications for enterprise adoption
Summary and Next Steps
Requirements
- Proficiency in machine learning workflows
- Strong foundation 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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