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Amazon Web Services

Building Language Models on AWS

Amazon Web Services and Amazon via AWS Skill Builder


Amazon SageMaker helps data scientists prepare, build, train, deploy, and monitor machine learning (ML) models. SageMaker brings together a broad set of capabilities, including access to distributed training libraries, open source models, and foundation models (FMs). This course introduces experienced data scientists to the challenges of building language models and the different storage, ingestion, and training options to process a large text corpus. The course also discusses the challenges of deploying large models and customizing foundational models for generative artificial intelligence (generative AI) tasks using Amazon SageMaker Jumpstart.

  • Course level: Advanced
  • Duration: 5.5 hours


This course includes text instruction, illustrative graphics, knowledge check questions, and video demonstrations of labs you can run in your own Amazon Web Services (AWS) account.

Course objectives

After completing this course, data scientists can confidently build, train, and tune

performant language models on AWS using SageMaker.

In this course, you will learn to do the following:

  • Apply best practices for storing and ingesting a large amount of text data to support distributed training
  • Explore data parallelism and model parallelism libraries to support distributed training on SageMaker
  • Explain the options available on SageMaker to improve training performance, such as Amazon SageMaker Training Compiler and Elastic Fabric Adapter (EFA)
  • Explore large language model (LLM) optimization techniques for effective model deployment
  • Demonstrate how to fine-tune foundational models available on SageMaker Jumpstart

Intended audience

This course is intended for the following roles:

  • Data scientists
  • ML engineers


We recommend that attendees of this course have:

  • More than 1 year of experience with natural language processing (NLP)
  • More than 1 year of experience with training and tuning language models
  • Intermediate-level proficiency in Python language programming
  • AWS Technical Essentials
  • Amazon SageMaker Studio for Data Scientists

Course outline

Course Series Introduction

Section 1: Introduction

  • Introduction to Building Language Models on AWS

Section 2: Large Language Model Basics

  • Types of Large Language Models
  • Common Generative AI Use Cases

Section 3: Course Series Outline

  • Topics Covered in Future Modules


Addressing the Challenges of Building Language Models

Section 1: Common Challenges

  • Common LLM Practitioner Challenges

Section 2: Multi-Machine Training Solutions

  • Scaling LLMs with Distributed Training
  • Applying Data Parallelism Techniques
  • Applying Model Parallelism Techniques

Section 3: Performance Optimization Solutions

  • Performance Optimization Techniques
  • Using Purpose-Built Infrastructure

Section 4: Wrap Up

  • Module Assessment


Using Amazon SageMaker for Training Language Models

Section 1: Configuring SageMaker Studio

  • SageMaker Basics
  • Setting up a SageMaker Studio Domain

Section 2: SageMaker Infrastructure

  • Choosing Compute Instance Types

Section 3: Working with the SageMaker Python SDK

  • SageMaker Python SDK Basics
  • Training and Deploying Language Models with the SageMaker Python SDK

Section 4: Wrap Up

  • Module Assessment


Demonstration - Setting up Amazon SageMaker Studio


Ingesting Language Model Data

Section 1: Preparing Data

  • Data Management Overview
  • Preparing Data for Ingestion

Section 2: Analyzing Data Ingestion Options

  • Loading Data with the SageMaker Python SDK
  • Ingesting Data from Amazon S3
  • Ingesting Data with FSx for Lustre
  • Additional Data Ingestion Options
  • Data Ingestion and Storage Considerations

Section 3: Wrap Up

  • Module Assessment

Training Large Language Models

Section 1: Creating a SageMaker Training Job

  • Launching SageMaker Training Jobs
  • Modifying Scripts for Script Mode

Section 2: Optimizing Your SageMaker Training Job

  • Monitoring and Troubleshooting
  • Optimizing Computational Performance
  • SageMaker Training Features for Language Model Training

Section 3: Using Distributed Training on SageMaker

  • SageMaker Distributed Training Support
  • Using the SageMaker Distributed Data Parallel Library
  • Using the SageMaker Model Parallel Library
  • Using the SageMaker Model Parallel Library and Sharded Data Parallelism
  • Training with the EFA

Section 4: Compiling Your Training Code

  • Using the SageMaker Training Compiler

Section 5: Wrap Up

  • Module Assessment

Demonstration - Training Your First Language Model with Amazon SageMaker


Demonstration - Data Parallel on SageMaker Training with PyTorch Lightning


Demonstration - Fine-tune GPT-2 with Near-Linear Scaling Using the Sharded Data Parallelism Technique in the Amazon SageMaker Model Parallelism Library


Deploying Language Models

Section 1: Deploying a Model in SageMaker

  • Introduction to SageMaker Deployment
  • Choosing a SageMaker Deployment Option

Section 2: Deploying Models for Inference

  • Real-Time Inference Overview
  • Using the SageMaker Python SDK for Model Deployment
  • Using the SageMaker Inference Recommender

Section 3: Deploying Large Language Models for Inference

  • Optimization Techniques
  • Model Compression Techniques
  • Model Partitioning
  • Optimized Kernels and Compilation
  • Deploying with SageMaker LMI Containers

Section 4: Additional Considerations

  • Other Considerations When Deploying Models on SageMaker

Section 5: Wrap Up

  • Module Assessment


Demonstration - Introduction to LLM Hosting on Amazon SageMaker with DeepSpeed Containers


Customizing Foundation Language Models for Generative AI Tasks

Section 1: Introduction

  • Introduction to Foundation Models

Section 2: Using SageMaker JumpStart

  • Getting Started with SageMaker JumpStart
  • Deploying SageMaker JumpStart Models with the SageMaker Python SDK
  • Selecting an FM

Section 3: Customizing FMs

  • Prompt Engineering
  • Fine-tune JumpStart Models with the SageMaker Python SDK

Section 4: Retrieval Augmented Generation (RAG)

  • Using Retrieval Augmented Generation (RAG)

Section 5: Wrap Up

  • Module Assessment

Demonstration - Deploy a FLAN-T5 Model for Text Generation Tasks Using Amazon SageMaker JumpStart


Call to Action and Additional Resources

Section 1: Review

  • Topics Covered in This Course Series

Section 2: Wrap Up

  • Resources, Recap, and Next Steps


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