Advanced Fine-Tuning & Prompt Management in Vertex AI Training Course
Vertex AI offers sophisticated tools for fine-tuning large language models and managing prompts, empowering developers and data teams to enhance model accuracy, streamline iteration processes, and ensure rigorous evaluation through its integrated libraries and services.
This instructor-led, live training session (available online or onsite) is designed for intermediate to advanced practitioners looking to improve the performance and reliability of generative AI applications using supervised fine-tuning, prompt versioning, and evaluation services within Vertex AI.
By the conclusion of this training, participants will be capable of:
- Applying supervised fine-tuning techniques to Gemini models in Vertex AI.
- Implementing prompt management workflows that include versioning and testing.
- Utilizing evaluation libraries to benchmark and optimize AI performance.
- Deploying and monitoring enhanced models in production environments.
Course Format
- Interactive lectures and discussions.
- Hands-on labs featuring Vertex AI fine-tuning and prompt tools.
- Case studies focused on enterprise model optimization.
Customization Options
- For customized training on this topic, please contact us to arrange.
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.
Open Training Courses require 5+ participants.
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Testimonials (1)
easy steps in ML
John Erick Baltazar - Globe telecom
Course - Vertex AI
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