Optimizing Large Models for Cost-Effective Fine-Tuning Training Course
Optimizing large models for fine-tuning is critical to making advanced AI applications feasible and cost-effective. This course focuses on strategies for reducing computational costs, including distributed training, model quantization, and hardware optimization, enabling participants to deploy and fine-tune large models efficiently.
This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to master techniques for optimizing large models for cost-effective fine-tuning in real-world scenarios.
By the end of this training, participants will be able to:
- Understand the challenges of fine-tuning large models.
- Apply distributed training techniques to large models.
- Leverage model quantization and pruning for efficiency.
- Optimize hardware utilization for fine-tuning tasks.
- Deploy fine-tuned models effectively in production environments.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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Course Outline
Introduction to Optimizing 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 like PyTorch or TensorFlow
- Familiarity with large language models and their applications
- Understanding of distributed computing concepts
Audience
- Machine learning engineers
- Cloud AI specialists
21 Hours
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