Advanced NLP: Training & Production Systems

Learn about dataset preparation, distributed training approaches, and optimization techniques for language models.

Understand key metrics for monitoring model training, and learn techniques for dataset preparation, enhancement, and quality filtering.

Learn about frameworks and approaches for distributed training, including DeepSpeed and FSDP, along with monitoring techniques.

Master approaches for efficiently fine-tuning large language models, including PEFT methods like LoRA and QLoRA.

Explore methods for aligning model outputs with human preferences, including DPO, PPO, and other alignment approaches.

Learn about automated benchmarks, human evaluation protocols, and model-based evaluation approaches for NLP systems.

Understand techniques for model quantization, from basic approaches to advanced methods like GGUF, GPTQ, and AWQ.

Learn about techniques for optimizing model inference, including flash attention, KV caching, and speculative decoding.

Build sophisticated RAG systems with chunking strategies, embeddings, rerankers, and vector databases for production deployment.

Dive into practical implementation details, optimization techniques, and deployment strategies for cutting-edge models like LLaMA, Mixtral, Mistral, and Claude.

Learn comprehensive strategies for deploying LLMs in production, including A/B testing, monitoring, scaling, and managing model versions.