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Duration 14 hours
Course Outline
Introduction to Advanced Model Customization
- Overview of fine-tuning and prompt management capabilities in Vertex AI
- Key use cases for model optimization
- Practical lab: configuring the Vertex AI workspace
Supervised Fine-Tuning of Gemini Models
- Structuring training data for fine-tuning
- Executing supervised fine-tuning pipelines
- Practical lab: fine-tuning a Gemini model
Prompt Engineering and Version Management
- Crafting effective prompts for generative AI
- Managing version control and ensuring reproducibility
- Practical lab: developing and testing prompt iterations
Evaluation and Benchmarking
- Introduction to Vertex AI evaluation libraries
- Automating testing and validation processes
- Practical lab: assessing prompts and generated outputs
Model Deployment and Monitoring
- Integrating refined models into applications
- Tracking performance and detecting drift
- Practical lab: deploying a fine-tuned model
Best Practices for Enterprise AI Optimization
- Managing scalability and costs
- Addressing ethical concerns and mitigating bias
- Case study: enhancing AI applications in production
Future Directions in Fine-Tuning and Prompt Management
- Emerging trends in LLM optimization
- Automated prompt adaptation and reinforcement learning
- Strategic impact on enterprise adoption
Summary and Next Steps
Requirements
- Background in machine learning workflows
- Proficiency in Python programming
- Understanding of cloud-based AI platforms
Target Audience
- AI engineers
- MLOps practitioners
- Data scientists
Testimonials (1)
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