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Course Outline
Introduction to Advanced Model Customization
- Overview of fine-tuning and prompt management features in Vertex AI
- Key use cases for model optimization
- Hands-on lab: Configuring the Vertex AI workspace
Supervised Fine-Tuning of Gemini Models
- Preparing high-quality training data for fine-tuning
- Executing supervised fine-tuning pipelines
- Hands-on lab: Fine-tuning a Gemini model
Prompt Engineering and Version Management
- Designing effective prompts for generative AI applications
- Managing version control and ensuring reproducibility
- Hands-on lab: Creating and testing different prompt versions
Evaluation and Benchmarking
- Understanding evaluation libraries available in Vertex AI
- Automating testing and validation processes
- Hands-on lab: Evaluating prompt efficacy and model outputs
Model Deployment and Monitoring
- Integrating optimized models into production applications
- Monitoring performance metrics and detecting drift
- Hands-on lab: Deploying a fine-tuned model
Best Practices for Enterprise AI Optimization
- Managing scalability and cost efficiency
- Addressing ethical considerations and mitigating bias
- Case study: Enhancing AI applications in production settings
Future Directions in Fine-Tuning and Prompt Management
- Emerging trends in LLM optimization
- Automated prompt adaptation and reinforcement learning techniques
- Strategic implications for enterprise adoption
Summary and Next Steps
Requirements
- Experience with machine learning workflows.
- Proficiency in Python programming.
- Familiarity with cloud-based AI platforms.
Target Audience
- AI engineers.
- MLOps practitioners.
- Data scientists.
14 Hours
Testimonials (1)
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