edX
edXLarge Language Model Operations (LLMOps)
2k studentsBEGINNEREnglish
Korshub's take
LLMs at Scale: From APIs to Operations
Prompt engineering is where most people stop—but production LLMs demand operations, security, and cost discipline. Master Azure and AWS deployment strategies, optimize GPU spend, build secure inference pipelines, and fine-tune open-source models like LLaMA and Mistral on custom datasets. Then scale the data infrastructure (Celery, RabbitMQ, Airflow, graph databases) that keeps models fed with clean data and customers served fast.
You'll master:
- Azure AI and AWS deployment patterns
- Cost and performance optimization
- Fine-tuning and containerization
- Data infrastructure at scale
About this course
Gain a solid understanding of generative AI models, their capabilities, and how to provide effective prompts for optimal outputs. ,Master Azure AI services, learn to manage GPU quotas, deploy LLMs, leverage Azure Machine Learning, and utilize Azure OpenAI Service. ,Deploy and manage LLMs on AWS, optimize cost and performance, monitor metrics, build secure pipelines, and comply with regulations. ,Leverage open-source LLMs like LLaMA and Mistral, fine-tune models on custom datasets, and containerize for efficient deployment. ,Scale data engineering systems using Celery, RabbitMQ, Airflow, and graph databases, optimizing performance for large, complex datasets.
Price
$2,743
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