Optimizing Large Models for Cost-Effective Fine-Tuning Training Course
Optimizing large models for fine-tuning is essential to making advanced AI applications feasible and cost-effective for government. 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 in a public sector context.
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 government scenarios.
By the end of this training, participants will be able to:
- Understand the challenges of fine-tuning large models for government use.
- Apply distributed training techniques to large models in a public sector environment.
- Leverage model quantization and pruning for efficiency in government applications.
- Optimize hardware utilization for fine-tuning tasks specific to government workflows.
- Deploy fine-tuned models effectively in production environments within the public sector.
Format of the Course
- Interactive lecture and discussion tailored for government professionals.
- Lots of exercises and practice relevant to public sector tasks.
- Hands-on implementation in a live-lab environment designed for government.
Course Customization Options
- To request a customized training for this course, specifically tailored for government needs, please contact us to arrange.
Course Outline
Introduction to Optimizing Large Models for Government
- Overview of large model architectures
- Challenges in fine-tuning large models within government environments
- Importance of cost-effective optimization strategies for government agencies
Distributed Training Techniques for Government
- Introduction to data and model parallelism for efficient government operations
- Frameworks for distributed training: PyTorch and TensorFlow, tailored for government use cases
- Scaling across multiple GPUs and nodes to enhance performance in government settings
Model Quantization and Pruning for Government
- Understanding quantization techniques for government applications
- Applying pruning to reduce model size while maintaining accuracy for government tasks
- Trade-offs between accuracy and efficiency in the context of government operations
Hardware Optimization for Government
- Choosing the right hardware for fine-tuning tasks in government agencies
- Optimizing GPU and TPU utilization to meet government performance requirements
- Using specialized accelerators for large models in government settings
Efficient Data Management for Government
- Strategies for managing large datasets within government systems
- Preprocessing and batching techniques to enhance performance for government applications
- Data augmentation methods to improve model robustness in government contexts
Deploying Optimized Models for Government
- Techniques for deploying fine-tuned models within government agencies
- Monitoring and maintaining model performance to ensure reliability in government operations
- Real-world examples of optimized model deployment in government settings
Advanced Optimization Techniques for Government
- Exploring low-rank adaptation (LoRA) for government-specific tasks
- Using adapters for modular fine-tuning to address specific government needs
- Future trends in model optimization relevant to government operations
Summary and Next Steps for Government
Requirements
- Experience with deep learning frameworks such as PyTorch or TensorFlow
- Familiarity with large language models and their applications in various contexts
- Understanding of distributed computing principles and techniques
Audience for Government
- Machine learning engineers working on public sector projects
- Cloud AI specialists supporting government initiatives
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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