Get in Touch

Course Outline

Foundations of Advanced Model Customization

  • Introduction to fine-tuning and prompt management mechanisms in Vertex AI
  • Key use cases for model performance enhancement
  • Practical lab: Initializing the Vertex AI workspace

Supervised Fine-Tuning for Gemini Models

  • Curating training datasets for fine-tuning
  • Executing supervised fine-tuning pipelines
  • Practical lab: Fine-tuning a Gemini model

Prompt Engineering and Version Control

  • Crafting high-impact prompts for generative AI
  • Managing version control to ensure reproducibility
  • Practical lab: Developing and validating prompt iterations

Evaluation and Benchmarking Strategies

  • Overview of evaluation libraries available in Vertex AI
  • Streamlining testing and validation processes
  • Practical lab: Assessing prompts and output quality

Model Deployment and Continuous Monitoring

  • Embedding optimized models into application architectures
  • Tracking performance metrics and detecting drift
  • Practical lab: Rolling out a fine-tuned model

Enterprise Best Practices for AI Optimization

  • Balancing scalability with cost efficiency
  • Addressing ethical considerations and mitigating bias
  • Case study: Enhancing AI applications in live production

Emerging Trends in Fine-Tuning and Prompt Management

  • New developments in LLM optimization
  • Automated prompt adaptation and reinforcement learning techniques
  • Strategic impact on enterprise adoption

Wrap-up and Recommended Pathways

Requirements

  • Proficiency in machine learning workflows
  • Solid understanding of Python programming
  • Exposure to cloud-native AI platforms

Target Audience

  • AI engineers
  • MLOps professionals
  • Data scientists
 14 Hours

Number of participants


Price per participant

Testimonials (1)

Upcoming Courses

Related Categories