다음에 대한 학습 방향 Technical Professionals
LLM Performance Optimization
This starter learning path explores advanced LLM performance optimization, covering fine-tuning (OpenAI, Hugging Face), model compression (quantization, pruning, distillation), parallelization strategies (data/model/hybrid/tensor/pipeline), and the latest techniques in efficient LLM deployment. Learners gain hands-on experience with tools like PyTorch, DeepSpeed, Megatron-LM, and practical workflows for scalable, accurate, and efficient GenAI model training and inference.
Learning objectives:
- Apply Fine-Tuning Techniques: Learn to fine-tune LLMs using OpenAI and Hugging Face Transformers for various NLP and vision tasks, including sentiment analysis, NER, summarization, and custom data adaptation.
- Implement Model Compression: Master quantization, pruning, and knowledge distillation to reduce model size and improve inference speed while maintaining accuracy.
- Leverage Parallelization Strategies: Understand and apply data, model, hybrid, pipeline, and tensor parallelism to train and deploy LLMs efficiently on multi-GPU and distributed systems.
- Utilize Advanced Optimization Methods: Explore domain adaptation, data augmentation, PEFT, LoRA, QLoRA, and the latest fine-tuning updates to further enhance LLM performance and scalability.
- Benchmark and Evaluate Optimized Models: Develop skills in evaluating, benchmarking, and comparing base versus optimized models to ensure robust, scalable, and accurate GenAI solutions.
Target audience:
This path is designed for machine learning engineers, data scientists, and AI practitioners seeking to optimize LLM performance for production-scale applications. It is ideal for professionals with foundational knowledge of deep learning and Python who want to deepen their expertise in fine-tuning, compression, and distributed training of GenAI models.