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Course Outline
Introduction to Advanced Model Customization
- Overview of fine-tuning and prompt management capabilities in Vertex AI
- Key use cases for model optimization
- Hands-on lab: Configuring the Vertex AI workspace
Supervised Fine-Tuning of Gemini Models
- Preparation of training datasets for fine-tuning
- Execution of supervised fine-tuning pipelines
- Hands-on lab: Fine-tuning a Gemini model
Prompt Engineering and Version Management
- Strategies for designing effective generative AI prompts
- Implementing version control for reproducibility
- Hands-on lab: Creating and testing prompt iterations
Evaluation and Benchmarking
- Introduction to evaluation libraries within Vertex AI
- Automation of testing and validation workflows
- Hands-on lab: Assessing prompts and model outputs
Model Deployment and Monitoring
- Integration of optimized models into applications
- Monitoring performance metrics and detecting drift
- Hands-on lab: Deploying a fine-tuned model
Best Practices for Enterprise AI Optimization
- Managing scalability and costs
- Addressing ethical considerations and mitigating bias
- 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 implications for enterprise adoption
Summary and Next Steps
Requirements
- Proficiency in machine learning workflows
- Working knowledge of Python programming
- Familiarity with cloud-based AI platforms
Target Audience
- AI Engineers
- MLOps Practitioners
- Data Scientists
14 Hours
Testimonials (1)
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