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Course Outline
Foundations of Advanced Model Customization
- Introduction to fine-tuning and prompt management capabilities in Vertex AI
- Real-world applications for model optimization
- Practical exercise: configuring the Vertex AI environment
Supervised Fine-Tuning for Gemini Models
- Curation and preparation of training datasets for fine-tuning
- Execution of supervised fine-tuning workflows
- Practical exercise: customizing a Gemini model
Prompt Design and Version Control
- Crafting high-impact prompts for generative AI systems
- Ensuring version integrity and result reproducibility
- Practical exercise: developing and validating prompt iterations
Performance Evaluation and Benchmarking
- Introduction to the evaluation toolkit within Vertex AI
- Automation of testing and verification processes
- Practical exercise: assessing prompts and model outputs
Production Deployment and Oversight
- Integrating refined models into live applications
- Monitoring operational performance and detecting drift
- Practical exercise: releasing a fine-tuned model
Enterprise Best Practices for AI Optimization
- Managing scalability and resource costs
- Addressing ethical standards and bias reduction
- Case analysis: enhancing AI performance in production
Future Trajectories in Fine-Tuning and Prompt Management
- Emerging innovations in LLM optimization
- Automated prompt adaptation and reinforcement learning techniques
- Strategic impact on enterprise integration
Conclusion and Path Forward
Requirements
- Practical experience with machine learning pipelines
- Proficiency in Python programming
- Working knowledge of cloud-native AI platforms
Intended Participants
- AI engineers
- MLOps specialists
- Data scientists
14 Hours
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