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Duration 14 hours
Course Outline
Foundations of Advanced Model Customization
- Introduction to fine-tuning and prompt management within Vertex AI
- Application scenarios for model optimization
- Practical exercise: Configuring the Vertex AI workspace
Supervised Fine-Tuning of Gemini Models
- Preparation of training data for fine-tuning
- Execution of supervised fine-tuning pipelines
- Practical exercise: Fine-tuning a Gemini model
Prompt Engineering and Version Control
- Structuring effective prompts for generative AI
- Implementing version control and ensuring reproducibility
- Practical exercise: Generating and validating prompt versions
Assessment and Benchmarking
- Review of evaluation libraries available in Vertex AI
- Automation of testing and validation procedures
- Practical exercise: Assessing prompts and model outputs
Model Deployment and Oversight
- Incorporating optimized models into application frameworks
- Tracking performance metrics and detecting drift
- Practical exercise: Deploying a fine-tuned model
Enterprise AI Optimization Best Practices
- Managing scalability and operational costs
- Addressing ethical implications and mitigating bias
- Case study: Enhancing AI applications in production environments
Future Trajectories in Fine-Tuning and Prompt Management
- Current trends in LLM optimization
- Automated prompt adaptation and reinforcement learning techniques
- Strategic impacts for enterprise integration
Recap and Subsequent Actions
Requirements
- Practical experience with machine learning processes
- Proficiency in Python programming
- Understanding of cloud-based AI platforms
Target Audience
- AI Engineers
- MLOps Specialists
- Data Scientists
Testimonials (1)
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