Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 14 hours
Course Outline
Foundations of Advanced Model Customization
- Insights into fine-tuning and prompt management within Vertex AI
- Practical applications for model optimization
- Practical exercise: Initializing the Vertex AI workspace
Supervised Fine-Tuning for Gemini Models
- Curating training datasets for effective fine-tuning
- Executing supervised fine-tuning pipelines
- Practical exercise: Refining a Gemini model
Prompt Engineering and Version Control
- Crafting high-impact prompts for generative AI
- Ensuring version control and result reproducibility
- Practical exercise: Developing and validating prompt iterations
Evaluation and Performance Benchmarking
- Exploring the evaluation library capabilities in Vertex AI
- Streamlining testing and validation processes
- Practical exercise: Assessing prompt efficacy and model outputs
Model Deployment and Continuous Monitoring
- Integrating refined models into application architectures
- Tracking performance metrics and detecting data drift
- Practical exercise: Releasing a fine-tuned model
Enterprise AI Optimization Best Practices
- Managing scalability and resource costs
- Addressing ethical standards and bias reduction
- Case analysis: Enhancing AI capabilities in production
Future Trajectories in Fine-Tuning and Prompt Management
- Current trends in Large Language Model (LLM) refinement
- Automated prompt adaptation and reinforcement learning techniques
- Strategic insights for enterprise integration
Conclusion and Path Forward
Requirements
- Proficiency in machine learning workflows
- Competence in Python programming
- Understanding of cloud-native AI platforms
Target Audience
- AI Engineers
- MLOps Specialists
- Data Scientists
Testimonials (1)
easy steps in ML
