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.
Download Links
Password: cms.ddpanda.org
