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Course Outline

Introduction to Advanced Model Customization

  • Understanding fine-tuning and prompt management capabilities in Vertex AI
  • Identifying key use cases for model optimization
  • Practical lab: Configuring the Vertex AI workspace

Supervised Fine-Tuning of Gemini Models

  • Curating and preparing training datasets for fine-tuning
  • Executing supervised fine-tuning pipelines
  • Practical lab: Performing fine-tuning on a Gemini model

Prompt Engineering and Version Management

  • Engineering effective prompts for generative AI tasks
  • Implementing version control for reproducibility
  • Practical lab: Developing and testing distinct prompt versions

Evaluation and Benchmarking

  • Exploring evaluation libraries available in Vertex AI
  • Streamlining testing and validation workflows
  • Practical lab: Assessing prompt effectiveness and model outputs

Model Deployment and Monitoring

  • Integrating optimized models into application architectures
  • Monitoring performance metrics and detecting drift
  • Practical lab: Deploying a fine-tuned model to production

Best Practices for Enterprise AI Optimization

  • Managing scalability and operational costs
  • Addressing ethical considerations and bias mitigation
  • Case study: Enhancing AI application performance in live environments

Future Directions in Fine-Tuning and Prompt Management

  • Emerging trends in Large Language Model (LLM) optimization
  • Automated prompt adaptation and reinforcement learning approaches
  • Strategic insights 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
 14 Hours

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