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Course Outline
Introduction to Advanced Model Customization
- Fundamentals of fine-tuning and prompt management in Vertex AI
- Practical applications for model optimization
- Hands-on exercise: configuring the Vertex AI workspace
Supervised Fine-Tuning of Gemini Models
- Curating and preparing training data for fine-tuning
- Executing supervised fine-tuning pipelines
- Hands-on exercise: fine-tuning a Gemini model
Prompt Engineering and Version Management
- Crafting high-impact prompts for generative AI
- Implementing version control to ensure reproducibility
- Hands-on exercise: generating and validating prompt versions
Evaluation and Benchmarking
- Exploring evaluation libraries available in Vertex AI
- Streamlining testing and validation workflows
- Hands-on exercise: assessing prompt efficacy and model outputs
Model Deployment and Monitoring
- Integrating optimized models into application architectures
- Monitoring performance metrics and detecting data drift
- Hands-on exercise: deploying a fine-tuned model
Best Practices for Enterprise AI Optimization
- Managing scalability and cost efficiency
- 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 Large Language Model optimization
- Automated prompt adaptation and reinforcement learning techniques
- Strategic impact on enterprise adoption strategies
Summary and Next Steps
Requirements
- Practical experience with machine learning workflows
- Proficiency in Python programming
- Understanding of cloud-based AI platforms
Target Audience
- AI Engineers
- MLOps Practitioners
- Data Scientists
14 Hours
Testimonials (1)
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