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Course Outline
Introduction to Advanced Model Customization
- Overview of fine-tuning and prompt management capabilities in Vertex AI
- Practical use cases for model optimization
- Hands-on lab: configuring the Vertex AI workspace
Supervised Fine-Tuning of Gemini Models
- Curating training data for effective fine-tuning
- Executing supervised fine-tuning pipelines
- Hands-on lab: applying fine-tuning to a Gemini model
Prompt Engineering and Version Management
- Crafting high-impact prompts for generative AI
- Maintaining version control and ensuring reproducibility
- Hands-on lab: generating and testing prompt iterations
Evaluation and Benchmarking
- Exploring evaluation libraries available in Vertex AI
- Streamlining testing and validation workflows
- Hands-on lab: assessing prompt effectiveness and outputs
Model Deployment and Monitoring
- Embedding optimized models into application architectures
- Tracking performance metrics and detecting drift
- Hands-on 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 applications in production environments
Future Directions in Fine-Tuning and Prompt Management
- Emerging trends in LLM optimization
- Automated prompt adaptation and reinforcement learning techniques
- Strategic impacts on enterprise adoption
Summary and Next Steps
Requirements
- Proficiency in machine learning workflows
- Solid understanding 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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