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

Master the in-demand AI skill that businesses want: to build and deploy customized LLMs. Learn to fine-tune open-source LLMs on proprietary data and deploy your customized LLM models using AWS SageMaker and Streamlit.

What you'll learn

• Fine-tune open-source LLMs for custom business purposes
• Deploy and scale models for enterprise purposes using AWS SageMaker and Streamlit
• Understand and implement QLoRA from theory to code
• Learn to preprocess proprietary datasets with chunking, tokenization, and attention masking
• Monitor training and performance to ensure optimal business results
• Manage cloud resources and optimize for cost
• Apply advanced AI engineering techniques including quantization and more

This isn’t just another “intro to AI” course. It’s a deep dive into the real-world skills that set AI Engineers apart.

You’ll learn how to fine-tune open-source large language models models on custom data, teaching you the skills needed to do the same with a business' proprietary or private data.

Plus you'll conduct fine-tuning using QLoRA - a game-changing technique that drastically cuts resource usage.

But you won’t stop there. Businesses need more from their LLMs. They demand more.

You’ll deploy your own custom LLMs using AWS tools like SageMaker, Lambda, and API Gateway, as well as Streamlit for the creating an easy-to-use user interface for the business' employees or customers.

Along the way, you’ll master concepts like bfloat16 training, dataset chunking, attention masks, HuggingFace’s Estimator API, and much more.

From theory to hands-on coding, this course gives you the full experience of building truly production-ready AI.

What Careers Does This Course Prepare Me For?

AI and machine learning are so hot right now. If you want to catch and ride the AI wave, customizing LLMs for business use cases is a great place to start. It's a skill that's used in a ton of in-demand careers that are at the forefront of Artificial Intelligence including:

AI Engineer & Machine Learning Engineer: Focuses on designing, developing, and customizing machine learning models and deploying them to production environments. Requires skills in model training, optimization, and deployment.

AI Specialist: Specializes in building applications using artificial intelligence technologies and machine learning models.

Data Scientist: Involves analyzing and interpreting complex data to help companies make informed decisions. Requires expertise in data preparation, exploratory data analysis, and model building.

AI Research Scientist: Conducts research to advance the field of artificial intelligence and machine learning. Requires deep understanding of advanced machine learning concepts, including attention mechanisms and large language models.

Cloud Engineer: Focuses on designing, planning, managing, maintaining, and supporting cloud computing applications. Requires knowledge of AWS services and best practices for cloud deployment.

DevOps Engineer: Bridges the gap between development and operations by automating the process of software delivery and infrastructure changes. Needs skills in deploying and monitoring machine learning models using tools like AWS CloudWatch.

Software Engineer: Involves developing software applications, including those with integrated machine learning components. Requires understanding of integrating machine learning models into applications and ensuring their scalability and performance.

Data Engineer: Focuses on building and maintaining data pipelines, ensuring data is clean, reliable, and ready for analysis. Requires knowledge of data storage solutions like AWS S3 and data preparation techniques.

Technical Product Manager: Manages the development and deployment of technology products, including those involving machine learning. Requires an understanding of the technical aspects of machine learning deployment and monitoring.