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Duration 14 hours
Course Outline
Foundations of Advanced Model Customization
- An examination of fine-tuning and prompt management capabilities in Vertex AI
- Strategic use cases for optimizing model performance
- Practical exercise: Configuring the Vertex AI development environment
Executing Supervised Fine-Tuning on Gemini
- Curating training datasets specifically for fine-tuning
- Orchestrating supervised fine-tuning processes
- Practical exercise: Refining a Gemini model’s capabilities
Prompt Architecture and Version Control
- Architecting high-impact prompts for generative systems
- Ensuring reproducibility through version management
- Practical exercise: Developing and validating prompt iterations
Performance Evaluation and Benchmarking
- An exploration of Vertex AI’s native evaluation toolkits
- Streamlining testing and verification workflows
- Practical exercise: Assessing prompt efficacy and model outputs
Production Deployment and Continuous Monitoring
- Seamlessly integrating optimized models into application stacks
- Tracking performance metrics and identifying model drift
- Practical exercise: Releasing a fine-tuned model to production
Enterprise-Grade AI Optimization Best Practices
- Managing scalability and resource costs effectively
- Navigating ethical frameworks and mitigating bias
- Case analysis: Enhancing live AI applications
Evolution of Fine-Tuning and Prompt Strategies
- Key emerging trends in LLM optimization
- Leveraging automated prompt adaptation and reinforcement learning
- Strategic outlook for enterprise AI integration
Conclusions and Path Forward
Requirements
- Proficiency in machine learning operational workflows
- Solid command of Python programming
- Working knowledge of cloud-native AI platforms
Target Learners
- AI Engineers
- MLOps Specialists
- Data Scientists
Testimonials (1)
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