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 Duration 14 hours

Course Outline

Introduction to Advanced Model Customization

  • Foundations of fine-tuning and prompt management within Vertex AI
  • Key application scenarios for model optimization
  • Practical lab: Initializing the Vertex AI workspace

Supervised Fine-Tuning of Gemini Models

  • Curating and preparing data for fine-tuning tasks
  • Executing supervised fine-tuning pipelines
  • Practical lab: Fine-tuning a Gemini model

Prompt Engineering and Version Control

  • Constructing high-impact prompts for generative AI
  • Implementing version control for reproducibility
  • Practical lab: Developing and testing prompt iterations

Evaluation and Benchmarking

  • Exploring evaluation libraries available in Vertex AI
  • Automating validation and testing procedures
  • Practical lab: Assessing prompt efficacy and model outputs

Model Deployment and Monitoring

  • Integrating refined models into live applications
  • Tracking performance metrics and detecting drift
  • Practical lab: Deploying a fine-tuned model to production

Enterprise AI Optimization Best Practices

  • Managing scalability and operational costs
  • Addressing ethical considerations and mitigating bias
  • Case study: Enhancing AI performance in production environments

Future Trends in Fine-Tuning and Prompt Management

  • Evolving trends in Large Language Model (LLM) optimization
  • Leveraging automated prompt adaptation and reinforcement learning
  • Strategic insights for enterprise-scale adoption

Conclusion and Path Forward

Requirements

  • Practical experience with machine learning pipelines
  • Proficiency in Python programming
  • Understanding of cloud-native AI platforms

Target Audience

  • AI Engineers
  • MLOps Specialists
  • Data Scientists

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