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
- Introduction to fine-tuning and prompt management strategies in Vertex AI.
- Exploring key use cases for model optimization.
- Practical exercise: Initializing the Vertex AI workspace.
Refining Gemini Models via Supervised Fine-Tuning
- Preparing and structuring training datasets for fine-tuning.
- Executing supervised fine-tuning pipelines.
- Practical exercise: Fine-tuning a Gemini model.
Prompt Engineering and Version Control
- Crafting effective prompts for generative AI applications.
- Managing version control to ensure reproducibility.
- Practical exercise: Developing and testing distinct prompt versions.
Evaluation and Performance Benchmarking
- Overview of evaluation libraries available in Vertex AI.
- Streamlining automated testing and validation processes.
- Practical exercise: Assessing prompt effectiveness and model outputs.
Deploying and Monitoring Models
- Seamlessly integrating optimized models into application architectures.
- Tracking performance metrics and detecting drift.
- Practical exercise: Deploying a fine-tuned model to production.
Enterprise AI Optimization Best Practices
- Managing scalability and cost efficiency.
- Addressing ethical considerations and mitigating bias.
- Case study: Enhancing the performance of AI applications in production settings.
Future Trajectories in Fine-Tuning and Prompt Management
- Emerging trends in Large Language Model (LLM) optimization.
- Automated prompt adaptation and the role of reinforcement learning.
- Strategic insights for enterprise adoption and growth.
Conclusion and Path Forward
Requirements
- Hands-on experience with machine learning workflows.
- Proficiency in Python programming.
- Working knowledge of cloud-based AI platforms.
Intended Audience
- AI Engineers
- MLOps Practitioners
- Data Scientists
Testimonials (1)
easy steps in ML
