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Course Outline
Foundations of Advanced Model Customization
- Introduction to fine-tuning and prompt management mechanisms in Vertex AI
- Key use cases for model performance enhancement
- Practical lab: Initializing the Vertex AI workspace
Supervised Fine-Tuning for Gemini Models
- Curating training datasets for fine-tuning
- Executing supervised fine-tuning pipelines
- Practical lab: Fine-tuning a Gemini model
Prompt Engineering and Version Control
- Crafting high-impact prompts for generative AI
- Managing version control to ensure reproducibility
- Practical lab: Developing and validating prompt iterations
Evaluation and Benchmarking Strategies
- Overview of evaluation libraries available in Vertex AI
- Streamlining testing and validation processes
- Practical lab: Assessing prompts and output quality
Model Deployment and Continuous Monitoring
- Embedding optimized models into application architectures
- Tracking performance metrics and detecting drift
- Practical lab: Rolling out a fine-tuned model
Enterprise Best Practices for AI Optimization
- Balancing scalability with cost efficiency
- Addressing ethical considerations and mitigating bias
- Case study: Enhancing AI applications in live production
Emerging Trends in Fine-Tuning and Prompt Management
- New developments in LLM optimization
- Automated prompt adaptation and reinforcement learning techniques
- Strategic impact on enterprise adoption
Wrap-up and Recommended Pathways
Requirements
- Proficiency in machine learning workflows
- Solid understanding of Python programming
- Exposure to cloud-native AI platforms
Target Audience
- AI engineers
- MLOps professionals
- Data scientists
14 Hours
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