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Course Outline
Introduction to Advanced Model Customisation
- Overview of fine-tuning and prompt management features in Vertex AI
- Key use cases for model optimisation
- Practical lab: configuring the Vertex AI workspace
Supervised Fine-Tuning of Gemini Models
- Preparing training datasets for fine-tuning
- Executing supervised fine-tuning pipelines
- Practical lab: fine-tuning a Gemini model
Prompt Engineering and Version Control
- Crafting effective prompts for generative AI
- Managing version control and ensuring reproducibility
- Practical lab: creating and testing prompt versions
Evaluation and Benchmarking
- Insight into evaluation libraries available in Vertex AI
- Automating testing and validation processes
- Practical lab: assessing prompts and outputs
Model Deployment and Monitoring
- Integrating optimised models into applications
- Tracking performance and detecting drift
- Practical lab: deploying a fine-tuned model
Best Practices for Enterprise AI Optimisation
- Managing scalability and costs
- Addressing ethical considerations and bias mitigation
- Case study: enhancing AI applications in production
Future Directions in Fine-Tuning and Prompt Management
- Evolving trends in LLM optimisation
- Automated prompt adaptation and reinforcement learning
- Strategic implications for enterprise adoption
Summary and Next Steps
Requirements
- Proficiency with machine learning workflows
- Working knowledge of Python programming
- Acquaintance with cloud-based AI platforms
Target Audience
- AI Engineers
- MLOps Practitioners
- Data Scientists
14 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 2600 € + VAT*
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