AI Engineering: Customizing LLMs for Business (Fine-Tuning LLMs with QLoRA & AWS)

Source: https://zerotomastery.io/courses/fine-tuning-llms-for-business/

Master an in-demand skill that companies are looking for: developing and implementing custom LLMs. In this course, you will learn how to fine-tune open large language models on closed/corporate data and deploy your models using AWS (SageMaker, Lambda, API Gateway) and Streamlit to provide a convenient interface for employees and clients.

This is not “just another introductory AI course.” It is a practical deep dive into the skills that set AI engineers apart on real projects. You will perform fine-tuning using QLoRA, a method that drastically reduces resource consumption, and then turn the model into a production service.

What you will master:

Fine-tuning open-source LLM on your own datasets (including corporate ones).
Practice with QLoRA, bfloat16 training, chunking datasets, attention masks.
The Hugging Face ecosystem (including Estimator API) and MLOps pipeline on AWS.
Model deployment and integration: SageMaker endpoints, Lambda, API Gateway, monitoring.
Creating a simple business UI on Streamlit.

Outcome: from theory to code and production – the complete development cycle of applied AI for business cases.

Who it benefits and what roles it prepares for:

AI Engineer / ML Engineer – designing, fine-tuning, and producing models.
AI Specialist – creating applied solutions based on AI.
Data Scientist – data preparation, EDA, and building models for company tasks.
AI Research Scientist – in-depth work with attention mechanisms and LLM.
Cloud Engineer – architecture and best deployment practices in AWS.
DevOps Engineer – automation, release, and monitoring of ML services (CloudWatch, etc.).
Software Engineer – integrating models into applications with scalability in mind.
Data Engineer – data pipelines, storage (S3), preprocessing.
Technical Product Manager – planning and releasing ML products, metrics, and monitoring.