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 Duration 21 hours

Course Outline

Introduction to Large Model Optimization

  • Overview of architectures in large models
  • Key challenges in fine-tuning large models
  • The significance of cost-effective optimization

Distributed Training Techniques

  • Foundations of data and model parallelism
  • Distributed training frameworks: PyTorch and TensorFlow
  • Scaling across multiple GPUs and nodes

Model Quantization and Pruning

  • Insights into quantization methods
  • Applying pruning to minimize model size
  • Balancing accuracy and efficiency trade-offs

Hardware Optimization

  • Selecting appropriate hardware for fine-tuning
  • Enhancing GPU and TPU utilization
  • Leveraging specialized accelerators for large models

Efficient Data Management

  • Strategies for handling large datasets
  • Preprocessing and batching for improved performance
  • Application of data augmentation techniques

Deploying Optimized Models

  • Methods for deploying fine-tuned models
  • Monitoring and sustaining model performance
  • Case studies of optimized model deployment

Advanced Optimization Techniques

  • Exploring low-rank adaptation (LoRA)
  • Implementing adapters for modular fine-tuning
  • Emerging trends in model optimization

Summary and Next Steps

Requirements

  • Proficiency in deep learning frameworks such as PyTorch or TensorFlow
  • Knowledge of large language models and their practical applications
  • A solid understanding of distributed computing concepts

Audience

  • Machine learning engineers
  • Cloud AI specialists

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