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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.

Target Audience

  • AI engineers.
  • MLOps practitioners.
  • Data scientists.
 14 Hours

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