Implement and manage the operational lifecycle of large language models (LLMs) in production environments. This course covers advanced techniques for infrastructure management, model deployment, performance optimization, and monitoring specific to the scale and complexity of LLMs. Learn to build robust, scalable, and cost-effective LLMOps pipelines.
Prerequisites ML and MLOps experience
Level:
LLM Infrastructure Design
Architect scalable infrastructure for training and serving large language models, considering GPU/TPU resources and networking.
Distributed Training Management
Implement and manage distributed training jobs for multi-billion parameter models using frameworks like DeepSpeed or Megatron-LM.
Efficient Fine-tuning Operations
Operationalize parameter-efficient fine-tuning (PEFT) techniques within MLOps workflows.
Advanced LLM Deployment
Deploy large models using optimized inference servers, quantization, and specialized serving patterns.
LLM Monitoring and Observability
Implement comprehensive monitoring strategies for LLM performance, cost, drift, and output quality.
Cost Optimization
Apply strategies to manage and optimize the significant costs associated with training and serving large models.
RAG System Operations
Manage the operational aspects of Retrieval-Augmented Generation systems, including vector database management.