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.
Duration 14 hours
Course Outline
Introduction to Advanced Model Customization
- Foundations of fine-tuning and prompt management within Vertex AI
- Key application scenarios for model optimization
- Practical lab: Initializing the Vertex AI workspace
Supervised Fine-Tuning of Gemini Models
- Curating and preparing data for fine-tuning tasks
- Executing supervised fine-tuning pipelines
- Practical lab: Fine-tuning a Gemini model
Prompt Engineering and Version Control
- Constructing high-impact prompts for generative AI
- Implementing version control for reproducibility
- Practical lab: Developing and testing prompt iterations
Evaluation and Benchmarking
- Exploring evaluation libraries available in Vertex AI
- Automating validation and testing procedures
- Practical lab: Assessing prompt efficacy and model outputs
Model Deployment and Monitoring
- Integrating refined models into live applications
- Tracking performance metrics and detecting drift
- Practical lab: Deploying a fine-tuned model to production
Enterprise AI Optimization Best Practices
- Managing scalability and operational costs
- Addressing ethical considerations and mitigating bias
- Case study: Enhancing AI performance in production environments
Future Trends in Fine-Tuning and Prompt Management
- Evolving trends in Large Language Model (LLM) optimization
- Leveraging automated prompt adaptation and reinforcement learning
- Strategic insights for enterprise-scale adoption
Conclusion and Path Forward
Requirements
- Practical experience with machine learning pipelines
- Proficiency in Python programming
- Understanding of cloud-native AI platforms
Target Audience
- AI Engineers
- MLOps Specialists
- Data Scientists
Testimonials (1)
easy steps in ML