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Course Outline
Foundations of Advanced Model Customization
- Introduction to fine-tuning and prompt management features in Vertex AI
- Applicable use cases for model optimization
- Practical exercise: Configuring the Vertex AI workspace
Supervised Fine-Tuning for Gemini Models
- Structuring training data for fine-tuning purposes
- Executing supervised fine-tuning pipelines
- Practical exercise: Fine-tuning a Gemini model
Prompt Engineering and Version Control
- Creating effective prompts for generative AI tasks
- Managing version control to ensure reproducibility
- Practical exercise: Developing and validating prompt iterations
Evaluation and Performance Benchmarking
- Overview of integrated evaluation libraries in Vertex AI
- Streamlining testing and validation processes
- Practical exercise: Assessing prompt efficacy and model outputs
Model Deployment and Continuous Monitoring
- Embedding optimized models into live applications
- Tracking performance metrics and identifying drift
- Practical exercise: Rolling out a fine-tuned model
Enterprise Best Practices for AI Optimization
- Managing scalability and operational costs
- Addressing ethical considerations and mitigating bias
- Case analysis: Enhancing AI applications in production environments
Future Trends in Fine-Tuning and Prompt Management
- Emerging trends in LLM optimization strategies
- Automated prompt adaptation and reinforcement learning techniques
- Strategic insights for enterprise integration
Conclusion and Recommended Next Steps
Requirements
- Practical experience with machine learning workflows
- Proficiency in Python programming
- Working knowledge of cloud-based AI platforms
Target Audience
- AI engineers
- MLOps specialists
- Data scientists
14 Hours
Testimonials (1)
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