Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Advanced Model Customization
- Overview of fine-tuning and prompt management in Vertex AI.
- Use cases for model optimization.
- Hands-on lab: setting up the Vertex AI workspace.
Supervised Fine-Tuning of Gemini Models
- Preparing training data for fine-tuning.
- Running supervised fine-tuning pipelines.
- Hands-on lab: fine-tuning a Gemini model.
Prompt Engineering and Version Management
- Designing effective prompts for generative AI.
- Version control and reproducibility.
- Hands-on lab: creating and testing prompt versions.
Evaluation and Benchmarking
- Overview of evaluation libraries in Vertex AI.
- Automating testing and validation workflows.
- Hands-on lab: evaluating prompts and outputs.
Model Deployment and Monitoring
- Integrating optimized models into applications.
- Monitoring performance and detecting drift.
- Hands-on lab: deploying a fine-tuned model.
Best Practices for Enterprise AI Optimization
- Scalability and cost management.
- Ethical considerations and bias mitigation.
- Case study: improving AI applications in production.
Future Directions in Fine-Tuning and Prompt Management
- Emerging trends in LLM optimization.
- Automated prompt adaptation and reinforcement learning.
- Strategic implications for enterprise adoption.
Summary and Next Steps
Requirements
- Experience with machine learning workflows.
- Knowledge of Python programming.
- Familiarity with cloud-based AI platforms.
Target Audience
- AI engineers.
- MLOps practitioners.
- Data scientists.
14 Hours
Testimonials (1)
easy steps in ML
