LLM Foundations & Deployment with Amazon SageMaker AI

Course Description

This advanced course provides a complete technical foundation for building and deploying large language models using Amazon SageMaker AI. Learners begin by understanding core LLM concepts such as tokenization, attention mechanisms, context windows, and scaling laws that drive modern foundation models.

The course explores the challenges of training large models, including catastrophic forgetting, GPU memory bottlenecks, and distributed compute requirements. Learners will configure high-performance SageMaker Studio environments and build scalable data ingestion pipelines using Amazon S3 and SageMaker Data Wrangler.

A major focus is placed on distributed training techniques, including data parallelism, tensor parallelism, and pipeline parallelism. Learners will work with SageMaker HyperPod to create resilient multi-node training clusters capable of supporting long-duration foundation model training jobs.

The course also covers alignment techniques such as Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), PPO, and Direct Preference Optimization (DPO). Learners will implement preference-based alignment workflows and optimize training using parameter-efficient fine-tuning approaches like LoRA.

Finally, the course dives into production deployment strategies using SageMaker Large Model Inference (LMI) containers, quantization techniques, speculative decoding, and optimized inference architectures for high-throughput serving.

The capstone project guides learners through building a fully custom-aligned LLM—from distributed fine-tuning on HyperPod to scalable deployment using SageMaker LMI containers.

Who is this course for

• Machine learning engineers working with LLMs

• AI researchers building foundation models

• MLOps engineers managing large-scale AI infrastructure

• Cloud architects designing AI platforms

• Advanced GenAI practitioners exploring distributed model training

Course Objectives

By the end of this course, learners will be able to:

• Understand LLM architectures, scaling principles, and distributed training strategies

• Build and optimize large-scale training pipelines using SageMaker AI and SageMaker HyperPod

• Configure GPU infrastructure, data parallelism, and model parallelism for resilient training workloads

• Apply advanced alignment and fine-tuning techniques including RLHF, PPO, DPO, and LoRA

• Optimize, deploy, monitor, and scale production-ready LLMs using quantization, speculative decoding, and SageMaker LMI containers

Prerequisites

• Strong Python programming skills

• Experience with deep learning frameworks such as PyTorch or TensorFlow

• Understanding of machine learning fundamentals

• Familiarity with AWS services and cloud infrastructure

• Prior exposure to transformer models and generative AI concepts recommended

Course outline

Section 1: LLM Foundations & Ingestion

1. LLM Basics: Tokenization, Attention Mechanisms, and Context Windows.

2. The Scaling Laws: Why we need distributed compute for modern LLMs.

3. Challenges: Catastrophic forgetting, data leakage, and GPU memory bottlenecks.

4. Lab 1: SageMaker Studio Setup. Configuring a high-performance JupyterLab environment with specialized EBS volumes for large datasets.

Section 2: Large-Scale Training on SageMaker

1. Data Ingestion: Using Amazon S3 and SageMaker Data Wrangler for PB-scale text datasets.

2. Managed Training: Understanding instance types (P4d/P5) and EBS optimization.

3. The @remote Decorator: Converting local Python functions into distributed SageMaker jobs with zero code changes.

4. Lab 2: The First Training Job. Launching a small-scale BERT or GPT-2 training run.

5. Lab 3: Remote Execution. Using the @remote decorator to offload a heavy preprocessing and training script to an multi-gpu(ml.p4d) instance.

Section 3: Distributed Training & HyperPod

1. Distributed Training: Data Parallelism vs. Model Parallelism (Tensor vs. Pipeline).

2. SageMaker HyperPod: Persistent, resilient clusters for training FM (Foundation Models) for months at a time.

3. HyperPod Fault Tolerance: Automatic node health checks and checkpoint auto-recovery.

4. Lab 4: Distributed Model Parallel. Implementing the SageMaker Model Parallel (SMP) library to split a 30B+ parameter model across multiple GPUs.

5. Lab 5: Building with HyperPod. Setting up a multi-node HyperPod cluster to run a long-duration pre-training job.

Section 4: Alignment & Human Feedback (RLHF)

1. Alignment Theory: Why pre-training isn't enough (SFT vs. RLHF).

2. RLHF Mechanics: Reward Models, Proximal Policy Optimization (PPO), and Direct Preference Optimization (DPO).

3. Multi-Adapter PPO: Efficiently aligning models using LoRA adapters.

4. Lab 6: E2E Preference Alignment. Using a dataset of human-ranked responses to train a Reward Model and update an LLM's policy via PPO.

Section 5: Optimized Deployment

1. Deployment Strategies: Real-time endpoints, Serverless Inference, and Batch Transform.

2. Large Model Inference (LMI): Using DJL (Deep Java Library) and LMI containers for high-throughput serving.

3. Optimization: Quantization (FP8/INT8), Paged Attention and Speculative Decoding.

4. Lab 7: Deploying Mixtral-8x7B. Using the SageMaker LMI container and an ml.g5 or ml.p4d instance to host the Mixtral Mixture-of-Experts model with high performance.

Section 6: Capstone Project

Project: The Custom-Aligned LLM Take a base open-source model, perform Distributed Fine-Tuning on a HyperPod cluster, align it using RLHF, and finally deploy it as a scalable endpoint using SageMaker LMI containers for production-grade inference.