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From ML to GenAI: Building Real-World Apps on AWS

Learn to scope ML projects, craft prompts, and build Bedrock-powered chatbots – even if you’re new to machine learning.

Intermediate
8 courses
25 hours ~
Certificate of completion
Intermediate
8 courses
25 hours ~
Certificate of completion

About

Bridge the gap between AI theory and working products.

Start with ML fundamentals for decision-makers and learn prompt-engineering best practices. From there, you’ll dive into practical machine learning on AWS, training models with SageMaker and building real-world generative AI applications using Amazon Bedrock and LangChain. You’ll also work with foundation models on Bedrock (RAG, agents, guardrails) and learn how large language models are trained and optimized on AWS. No heavy math is required, and all steps are demonstrated in a sandbox environment.

By the end, you’ll have:

  • A Bedrock-powered chatbot or RAG assistant running with guardrails and cost tracking.
  • A project brief, data readiness checklist, and ROI storyboard ready to present to stakeholders.
  • Certificates confirming your ability to scope, prototype, and pitch LLM solutions.

AWS icon

What is a Skill Path?

Built in collaboration with AWS, Skill Paths are integrated learning journeys that combine JetBrains IDE projects, AWS video lessons, and hands-on cloud Labs to give you real-world practice.

Content

1

Introduction to Machine Learning: Art of the Possible

AWS course

A 30-minute primer for business decision makers on ML fundamentals. Learn core concepts, common use cases, and how to weigh benefits and risks for your scenarios. Includes short presentations, videos, and quick knowledge checks to help you assess where ML fits in your business.

2

Machine Learning Terminology and Process

AWS course

This course introduces basic machine learning concepts and the process data goes through. You’ll explore each step of the ML process in detail, learn the key terms used in ML projects, and review common techniques applied at each stage.

3

Planning a Machine Learning Project

AWS course

Learn how to determine whether machine learning is the right solution for a business problem. You’ll identify the data, time, and production requirements needed for a successful ML project and outline when ML is appropriate.

4

Python Libraries – NumPy

In-IDE course

Intro to NumPy for learners with basic Python skills. Learn how ndarrays work, perform common operations (indexing, slicing, broadcasting, vectorization), and explore practical uses. The course is hands-on with examples and exercises, and follows the official NumPy documentation.

5

Amazon Bedrock Getting Started

AWS course

Explore Amazon Bedrock, AWS’s fully managed service for building and scaling generative artificial intelligence (AI) apps with leading foundation models. This intro walks through core concepts, benefits, common use cases, solution architecture patterns, and the cost model. You’ll also follow a guided, step-by-step console tutorial (with video and transcript) to stand up a simple chatbot in your own AWS account.

6

Mastering Large Language Models

In-IDE course

Get a comprehensive hands-on experience, starting with NLP basics, and moving on to advanced topics like fine-tuning, text generation, and more. This course combines theoretical understanding with extensive practical implementation, ensuring students can apply LLM techniques in real-world scenarios.

7

Building Language Models on AWS

AWS course

A deep dive for experienced data scientists and ML engineers on building small-to-large LLMs with Amazon SageMaker. Learn how to store and ingest a large amount of text data, run distributed training with data/model parallelism, and leverage SageMaker HyperPod and Elastic Fabric Adapter (EFA) to speed up scale-out training.

You’ll also cover aligning models with human feedback on SageMaker, plus deployment challenges and inference optimizations for production. The course blends explanations, graphics, knowledge checks, and video demos you can replicate in your own AWS account.

8

Building Generative AI Applications Using Amazon Bedrock

AWS course

Learn to design and ship GenAI apps with Amazon Bedrock using Bedrock APIs or AWS-LangChain integration. You’ll cover core architecture patterns for text generation, summarization, RAG, and Q&A. You learn to build RAG application using Amazon Bedrock Knowledge Bases, and AI Assistants that use knowledge bases and user-developed tools to answer questions using Amazon Bedrock Agents.

You’ll also learn to implement safeguards customized to your application requirements and responsible AI policies using Amazon Bedrock Guardrails. Along the way, you’ll apply LangChain components (prompt templates, chains, retrievers, agents) in interactive lessons and labs.

Get real experience with real developer tools

Practice with AWS tools
Learn directly in JetBrains IDE
Follow one integrated Skill Path
Earn certificate of completion

  • Practice with AWS tools

    Develop and apply practical skills with instructions for common cloud scenarios in a live AWS environment, without the risk of unanticipated expenses.

  • Learn directly in JetBrains IDE

    Gain practical experience with the tools and workflows you'll use in your career, simplifying the transition to real-world projects.

  • Follow one integrated Skill Path

    Move smoothly between IDE projects, AWS videos, and guided AWS Builder Labs – no extra logins required. Every step is sequenced and includes progress tracking.

  • Earn certificate of completion

    When you finish the path, you can download a certificate co-branded by JetBrains Academy and AWS.

Unlock the full
Skill Path experience

Start your 7-day free trial – upgrade anytime to unlock sandbox AWS Labs.

Skill Path PRO

Labs and Certificates.

per month billed yearly.
The tax rate depends on your country tax rules, entered tax identification number (e.g. VAT ID), and selected purchase method.

Full access to all Skill Paths

Professional JetBrains IDE environment

Guided AWS Builder Labs*

Certificate of completion*

Cancel anytime – keep free access

* Labs and certificates unlock after the free trial ends.

Enterprise

For agencies and companies.

Custom

Volume pricing starts at 30 users.
We tailor seats, security, and support to your organization.

Request team pricing

Full accesses to all Skill Paths

Professional JetBrains IDE environment

Guided AWS Builder Labs

Certificate of completion

Dedicated CSM and priority support

FAQ and troubleshooting

All IDE-based projects, as well as video courses, progress tracking, and certificates. AWS hands-on Labs unlock with PRO.
It depends on the path, but you can expect to work with core services such as ECS, ECR, EC2, CodeDeploy, SageMaker, and Bedrock.
Labs opens a prepaid sandbox account for you, so you don’t need your own AWS account.
Most learners finish in 20–40 hours, spread over their evenings or weekends.
Just JetBrains Toolbox 2.7+ and your preferred IDE – everything else runs in the cloud.
Yes, each path ends with a deployable project you can demo live.
Yes, you can skip the two Labs that require a paid subscription and still earn a JetBrains certificate.
Yes, we recommend our free Introduction to Python course first.
All cloud resources run in prepaid sandboxes, so there are no unexpected charges.
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Still not sure? Check our free Skill Paths first

Explore our cloud-native DevOps or AI and LLM Skill Paths next.All Skill Paths

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Build and Ship Cloud-Native Python Apps

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  • Beginner
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Skill PathFree

Launch Your First Full-Stack App on AWS

Code the frontend and backend, containerize with Docker, and deploy to AWS, all in one guided Skill Path that starts with “Hello, World!” and ends in the cloud.

  • Beginner
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Ready to engineer your own LLM?

Learn to train your first model today

Build on the basics.

If you’re just starting out, complete our free Introduction to Python course inside your IDE, then return here to tackle full-stack cloud deployment.