Optimizing Large Models for Cost-Effective Fine-Tuning Training Course
Optimising large models for fine-tuning is essential to making advanced AI applications both feasible and cost-effective. This course focuses on strategies to reduce computational expenses, including distributed training, model quantization, and hardware optimization, empowering participants to deploy and fine-tune large models efficiently.
This instructor-led, live training (available online or onsite) is designed for advanced-level professionals who wish to master techniques for optimizing large models to achieve cost-effective fine-tuning in real-world scenarios.
By the end of this training, participants will be able to:
- Understand the challenges associated with fine-tuning large models.
- Apply distributed training techniques to large models.
- Leverage model quantization and pruning to enhance efficiency.
- Optimize hardware utilization for fine-tuning tasks.
- Deploy fine-tuned models effectively within production environments.
Format of the Course
- Interactive lecture and discussion.
- Extensive exercises and practice sessions.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training session for this course, please contact us to arrange.
Course Outline
Introduction to Optimising Large Models
- Overview of large model architectures.
- Challenges in fine-tuning large models.
- Importance of cost-effective optimization.
Distributed Training Techniques
- Introduction to data and model parallelism.
- Frameworks for distributed training: PyTorch and TensorFlow.
- Scaling across multiple GPUs and nodes.
Model Quantization and Pruning
- Understanding quantization techniques.
- Applying pruning to reduce model size.
- Trade-offs between accuracy and efficiency.
Hardware Optimization
- Choosing the right hardware for fine-tuning tasks.
- Optimizing GPU and TPU utilization.
- Using specialized accelerators for large models.
Efficient Data Management
- Strategies for managing large datasets.
- Preprocessing and batching for performance.
- Data augmentation techniques.
Deploying Optimized Models
- Techniques for deploying fine-tuned models.
- Monitoring and maintaining model performance.
- Real-world examples of optimized model deployment.
Advanced Optimization Techniques
- Exploring low-rank adaptation (LoRA).
- Using adapters for modular fine-tuning.
- Future trends in model optimization.
Summary and Next Steps
Requirements
- Experience with deep learning frameworks such as PyTorch or TensorFlow.
- Familiarity with large language models and their applications.
- Understanding of distributed computing concepts.
Audience
- Machine learning engineers.
- Cloud AI specialists.
Open Training Courses require 5+ participants.
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