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Course Outline
Introduction to Advanced Model Customization
- Fundamentals of fine-tuning and prompt management in Vertex AI
- Key use cases for optimizing model performance
- Practical session: configuring the Vertex AI workspace
Supervised Fine-Tuning of Gemini Models
- Strategies for preparing high-quality training data
- Executing supervised fine-tuning pipelines
- Practical session: fine-tuning a Gemini model
Prompt Engineering and Version Management
- Crafting effective prompts for generative AI tasks
- Managing version control to ensure reproducibility
- Practical session: creating and validating prompt versions
Evaluation and Benchmarking
- Exploring evaluation libraries available in Vertex AI
- Automating testing and validation processes
- Practical session: assessing prompt quality and model outputs
Model Deployment and Monitoring
- Integrating optimized models into production applications
- Tracking performance metrics and detecting drift
- Practical session: 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 live environments
Future Directions in Fine-Tuning and Prompt Management
- Emerging trends in LLM optimization
- Advanced techniques including automated prompt adaptation and reinforcement learning
- Strategic impact on enterprise AI 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
14 Hours
Testimonials (1)
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