Large Language Models (LLMs) are redefining what’s possible with AI — from chatbots to code generation — but the barrier to training and deploying them is still high. Expensive hardware, massive memory requirements, and complex toolchains often block individual practitioners and small teams. This course is built to change that.
In this hands-on, code-first training, you’ll learn how to fine-tune models like Mixtral-8x7B using QLoRA — a state-of-the-art method that enables efficient training by combining 4-bit quantization, LoRA adapters, and double quantization. You’ll also gain a deep understanding of quantized arithmetic, floating-point formats (like bfloat16 and INT8), and how they impact model size, memory bandwidth, and matrix multiplication operations.
You’ll write advanced Python code to preprocess datasets with custom token-aware chunking strategies, dynamically identify quantizable layers, and inject adapter modules using the PEFT (Parameter-Efficient Fine-Tuning) library. You’ll configure and launch distributed fine-tuning jobs on AWS SageMaker, leveraging powerful multi-GPU instances and optimizing them using gradient checkpointing, mixed-precision training, and bitsandbytes quantization.
After training, you’ll go all the way to deployment: merging adapter weights, saving your model for inference, and deploying it via SageMaker Endpoints. You’ll then expose your model through an AWS Lambda function and an API Gateway, and finally, build a Streamlit application to create a clean, responsive frontend interface.
Whether you’re a machine learning engineer, backend developer, or AI practitioner aiming to level up — this course will teach you how to move from academic toy models to real-world, scalable, production-ready LLMs using tools that today’s top companies rely on.
- Machine Learning Engineers
- Backend and MLOps Engineers
- AI Researchers and Students
- Anyone who wants to go beyond “prompt engineering” and start building, training, and deploying their own production-ready LLMs.
