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
 14 Hours

Delivery Options

Private Group Training

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  • Training scheduled on a date of your choice
  • Delivered online, onsite/classroom or hybrid by experts sharing real world experience

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