Learning Paths for 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.

Skills:

LLM fine-tuning (OpenAI, Hugging Face)

Model compression techniques

Knowledge distillation

Parallel and distributed training (data/model/hybrid/tensor/pipeline parallelism)

GPU architecture and optimization

Domain adaptation and data augmentation

PEFT, LoRA, QLoRA techniques

Model evaluation and benchmarking

Fundamentals for AI and LLMs

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.

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