Amazon Launched FREE AI Courses & Get Free Certificate: Building Language Models on AWS


Recently amazon Launched an e-commerce company that launched Free AI courses And Got a free Certificate In the ever-evolving realm of artificial intelligence (AI), language models (LMs) have emerged as powerful tools capable of generating text, translating languages, and answering questions in an informative way. While building LMs traditionally required extensive technical expertise,it has introduced a free online course, “Building Language Models on AWS,” designed to democratize LM development and empower individuals to create their own AI language companions.

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Amazon Launched FREE AI Courses & Get Free Certificate: Building Language Models on AWS

About the Amazon

It was founded by Jeff Bezos from his garage in Bellevue, Washington, on July 5, 1994. Initially an online marketplace for books, it has expanded into a multitude of product categories, a strategy that has earned it the moniker The Everything Store. It has multiple subsidiaries including Web Services (cloud computing), Zoox (autonomous vehicles), Kuiper Systems (satellite Internet), and Lab126 (computer hardware R&D). Its other subsidiaries include Ring, Twitch, IMDb, and Whole Foods Market. Its acquisition of Whole Foods in August 2017 for US$13.4 billion substantially increased its footprint as a physical retailer.

About the Free AI Courses Program

It 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 SageMaker Jumpstart.

  • Course level: Advanced
  • Duration: 5.5 hours

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This course includes text instruction, illustrative graphics, knowledge check questions, and video demonstrations of labs you can run in your own 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 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
  • 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 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

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How To Apply?

Click on the below button to apply for the Amazon

Official Notification by Amazon for these Free Courses:


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