Advanced NLP: Training & Production Systems
Advanced NLP: Training & Production Systems
Master the engineering and production aspects of Natural Language Processing. Learn to train, fine-tune, optimize, and deploy language models at scale. This course covers everything from distributed training to production monitoring.
Learning Objectives
- Master training fundamentals and distributed training techniques
- Implement advanced fine-tuning methods including PEFT and LoRA
- Design and implement preference alignment and RLHF systems
- Optimize models through quantization and inference acceleration
- Build production RAG systems with vector databases
- Deploy and monitor language models in production environments
Lessons
Training Fundamentals and Optimization
90 minLearn about dataset preparation, distributed training approaches, and optimization techniques for language models.
Start Lesson →Training Monitoring and Dataset Engineering
60 minUnderstand key metrics for monitoring model training, and learn techniques for dataset preparation, enhancement, and quality filtering.
Start Lesson →Distributed Training Infrastructure
60 minLearn about frameworks and approaches for distributed training, including DeepSpeed and FSDP, along with monitoring techniques.
Start Lesson →Fine-tuning Techniques and Parameter-Efficient Methods
75 minMaster approaches for efficiently fine-tuning large language models, including PEFT methods like LoRA and QLoRA.
Start Lesson →Preference Alignment and RLHF
60 minExplore methods for aligning model outputs with human preferences, including DPO, PPO, and other alignment approaches.
Start Lesson →Comprehensive Model Evaluation
45 minLearn about automated benchmarks, human evaluation protocols, and model-based evaluation approaches for NLP systems.
Start Lesson →Model Quantization and Compression
60 minUnderstand techniques for model quantization, from basic approaches to advanced methods like GGUF, GPTQ, and AWQ.
Start Lesson →Inference Optimization Strategies
45 minLearn about techniques for optimizing model inference, including flash attention, KV caching, and speculative decoding.
Start Lesson →Production RAG Systems
75 minBuild sophisticated RAG systems with chunking strategies, embeddings, rerankers, and vector databases for production deployment.
Start Lesson →Advanced Model Implementations
75 minDive into practical implementation details, optimization techniques, and deployment strategies for cutting-edge models like LLaMA, Mixtral, Mistral, and Claude.
Start Lesson →Production Deployment and Operations
60 minLearn comprehensive strategies for deploying LLMs in production, including A/B testing, monitoring, scaling, and managing model versions.
Start Lesson →