Fine-Tune & Deploy Llms With Qlora On Sagemaker + Streamlit
 

Train/Fine Tune LLMs in AWS Sagemaker using QLoRA and advanced 4-bit quantization on your own dataset
Create an interactive Streamlit app to deploy your fine tuned LLM with Sagemaker, Lambda Functions, and API Gateway
Master QLoRA fine-tuning - including adapter injection, memory optimization, parameter freezing, and the mathematics behind it
Leverage bfloat16 compute types for faster and more efficient training on modern GPUs
Understand mixed precision training with qLoRA in Sagemaker
Use Parameter Efficient Fine Tuning(PEFT) to dynamically find and inject LoRA layers
Understand the entire low-level fine-tuning pipeline - from raw dataset to trained model
Use double quantization and nf4 precision to compress models without losing performance
Discover how gradient checkpointing drastically reduces VRAM usage during training
Fine-tune large models like Mixtral on Amazon SageMaker using state-of-the-art GPU acceleration
Understand custom chunking code for LLMs
Merge LoRA weights and unload adapters for final model export - ready for deployment
Deploy your trained model to SageMaker Endpoints using Amazon's production infrastructure
Build real-time LLM APIs using Lambda functions and API Gateway
Securely Set up Training Jobs with IAM roles
AWS Budgeting, Server Management, and Pricing
Learn how to use AWS Quotas to use powerful GPUs
Requirements
Familiarity with Python
Basic Linear Algebra(matrix multiplication)
Description
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.