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
- Overview of fine-tuning and prompt management capabilities in Vertex AI
- Key use cases for model optimisation
- Practical lab: Configuring the Vertex AI workspace
Supervised Fine-Tuning of Gemini Models
- Preparing training data for effective fine-tuning
- Executing supervised fine-tuning pipelines
- Practical lab: Fine-tuning a Gemini model
Prompt Engineering and Version Management
- Designing effective prompts for generative AI applications
- Implementing version control and ensuring reproducibility
- Practical lab: Creating and testing prompt versions
Evaluation and Benchmarking
- Introduction to evaluation libraries available in Vertex AI
- Automating testing and validation processes
- Practical lab: Evaluating prompts and model outputs
Model Deployment and Monitoring
- Integrating optimised models into application architectures
- Monitoring performance metrics and detecting drift
- Practical lab: Deploying a fine-tuned model
Best Practices for Enterprise AI Optimization
- Strategies for scalability and cost management
- Ethical considerations and methods for bias mitigation
- Case study: Enhancing AI applications in production environments
Future Directions in Fine-Tuning and Prompt Management
- Emerging trends in LLM optimisation
- Automated prompt adaptation and reinforcement learning techniques
- Strategic implications for enterprise adoption
Summary and 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 Practitioners
- Data Scientists
Testimonials (1)
easy steps in ML
