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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 optimizing model performance
- Hands-on lab: Configuring the Vertex AI workspace
Supervised Fine-Tuning of Gemini Models
- Preparing high-quality training data for fine-tuning
- Executing supervised fine-tuning pipelines
- Hands-on lab: Fine-tuning a specific Gemini model
Prompt Engineering and Version Management
- Crafting effective prompts for generative AI applications
- Implementing version control and ensuring reproducibility
- Hands-on lab: Developing and testing different prompt versions
Evaluation and Benchmarking
- Exploration of evaluation libraries available in Vertex AI
- Automating testing and validation procedures
- Hands-on lab: Assessing prompt efficacy and model outputs
Model Deployment and Monitoring
- Integrating optimized models into existing applications
- Tracking performance metrics and detecting drift
- Hands-on lab: Deploying a fine-tuned model to production
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 Large Language Model (LLM) optimization
- Automated prompt adaptation and reinforcement learning techniques
- Strategic implications for enterprise adoption and growth
Summary and Next Steps
Requirements
- Demonstrated experience with machine learning workflows
- Solid proficiency in Python programming
- Familiarity with cloud-based AI infrastructure and platforms
Target Audience
- AI Engineers
- MLOps Practitioners
- Data Scientists
14 Hours
Testimonials (1)
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