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Course Outline
Introduction to Advanced Model Customization
- Understanding fine-tuning and prompt management capabilities in Vertex AI
- Identifying key use cases for model optimization
- Practical lab: Configuring the Vertex AI workspace
Supervised Fine-Tuning of Gemini Models
- Curating and preparing training datasets for fine-tuning
- Executing supervised fine-tuning pipelines
- Practical lab: Performing fine-tuning on a Gemini model
Prompt Engineering and Version Management
- Engineering effective prompts for generative AI tasks
- Implementing version control for reproducibility
- Practical lab: Developing and testing distinct prompt versions
Evaluation and Benchmarking
- Exploring evaluation libraries available in Vertex AI
- Streamlining testing and validation workflows
- Practical lab: Assessing prompt effectiveness and model outputs
Model Deployment and Monitoring
- Integrating optimized models into application architectures
- Monitoring performance metrics and detecting drift
- Practical lab: Deploying a fine-tuned model to production
Best Practices for Enterprise AI Optimization
- Managing scalability and operational costs
- Addressing ethical considerations and bias mitigation
- Case study: Enhancing AI application performance in live environments
Future Directions in Fine-Tuning and Prompt Management
- Emerging trends in Large Language Model (LLM) optimization
- Automated prompt adaptation and reinforcement learning approaches
- Strategic insights 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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