Advanced Fine-Tuning & Prompt Management in Vertex AI Training Course
Vertex AI offers sophisticated tools for fine-tuning large models and managing prompts, empowering developers and data teams to enhance model accuracy, streamline iteration workflows, and ensure rigorous evaluation through built-in libraries and services.
This instructor-led, live training (available online or onsite) targets intermediate to advanced practitioners aiming to improve the performance and reliability of generative AI applications using supervised fine-tuning, prompt versioning, and evaluation services within Vertex AI.
Upon completion of this training, participants will be able to:
- Apply supervised fine-tuning techniques to Gemini models in Vertex AI.
- Implement prompt management workflows that include versioning and testing.
- Leverage evaluation libraries to benchmark and optimize AI performance.
- Deploy and monitor improved models in production environments.
Course Format
- Interactive lectures and discussions.
- Hands-on labs featuring Vertex AI fine-tuning and prompt tools.
- Case studies focused on enterprise model optimization.
Course Customization Options
- To request customized training for this course, please contact us to arrange.
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 drift detection
- 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
Audience
- AI engineers
- MLops practitioners
- Data scientists
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Testimonials (1)
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John Erick Baltazar - Globe telecom
Course - Vertex AI
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