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Course Outline

Introduction to Advanced Model Customization

  • Overview of fine-tuning and prompt management capabilities in Vertex AI
  • Key use cases for optimizing model performance
  • Hands-on lab: Configuring the Vertex AI workspace

Supervised Fine-Tuning of Gemini Models

  • Preparing high-quality training data for fine-tuning
  • Executing supervised fine-tuning pipelines
  • Hands-on lab: Fine-tuning a specific Gemini model

Prompt Engineering and Version Management

  • Crafting effective prompts for generative AI applications
  • Implementing version control and ensuring reproducibility
  • Hands-on lab: Developing and testing different prompt versions

Evaluation and Benchmarking

  • Exploration of evaluation libraries available in Vertex AI
  • Automating testing and validation procedures
  • Hands-on lab: Assessing prompt efficacy and model outputs

Model Deployment and Monitoring

  • Integrating optimized models into existing applications
  • Tracking performance metrics and detecting drift
  • Hands-on lab: Deploying a fine-tuned model to production

Best Practices for Enterprise AI Optimization

  • Managing scalability and operational costs
  • Addressing ethical considerations and mitigating bias
  • 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 techniques
  • Strategic implications for enterprise adoption and growth

Summary and Next Steps

Requirements

  • Demonstrated experience with machine learning workflows
  • Solid proficiency in Python programming
  • Familiarity with cloud-based AI infrastructure and platforms

Target Audience

  • AI Engineers
  • MLOps Practitioners
  • Data Scientists
 14 Hours

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