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