onlincourse

Generative AI (Gen AI) and LLMs on AWS

Gain practical skills in developing and deploying generative AI and LLM solutions using AWS services to help advance your career from Pragmatic AI Labs.

Duration

4 weeks

Weekly study

4 hours

100% online

How it works

Unlimited subscription

Learn more

Develop industry-ready AI skills

In this eight-week course, you’ll dive into the essentials of AWS for AI and gain an introduction to the platform’s wide capabilities.

Through practical learning, you’ll cover key AWS AI services and create your first AI project. This will help you develop industry-relevant skills and gain hands-on experience building LLM pipelines.

With your exposure to cutting-edge AWS technologies, you’ll be more competitive in the job market.

Learn how to code with CodeWhisperer

You’ll be introduced to AI pair programming, starting with an exploration of AI-assisted coding principles and best practices.

Using CodeWhisperer, you’ll complete a coding project with enhanced efficiency and accuracy. Through practical sessions, you’ll refine your AI-assisted coding techniques.

Gain skills in using Amazon Bedrock

Next, you’ll gain hands-on experience with Amazon Bedrock – an essential tool for developing robust LLM pipelines.

Covering various components of an LLM pipeline, you’ll learn how to build and deploy your own, gaining invaluable experience for developing advanced AI solutions.

Understand LLM solutions

On the final week, you’ll unpack the production process of LLMs on AWS.

Delving into the best practices for deploying LLMs in production environments, you’ll learn how to scale and optimise solutions effectively.

During a final project, you’ll develop a production-ready LLM solution, cementing the knowledge and skills you acquired over the course.

By the end, you’ll be able to address real-world AI challenges and implement production-ready solutions.

Week 1
Getting Started with Developing on AWS
- Getting Started
  Meet your instructor and get oriented to the course. Learn about the structure, expectations, and discussion etiquette. Introduce yourself to fellow learners in the meet-and-greet discussion. Prepare for your journey into AI.
- Introduction to AWS Cloud Computing for AI
  Explore cloud service and deployment models for AI. Discover the benefits of cloud computing and AWS's Cloud Adoption Framework for AI. Learn key concepts through readings, videos, and discussions.
- Set Up AI Focused Development Environments
  Learn to set up AI-focused development environments using Rust and Python. Explore MLOps, generative AI workflows, and AWS tools like SageMaker and Lightsail. Gain hands-on experience with labs and tutorials in both languages.
- Developing Serverless Solutions for Data, ML and AI
  Explore serverless solutions for data, ML, and AI using AWS services like Bedrock. Learn about serverless options for Rust, build a microservice with Axum, and understand Docker workflows. Includes demos, diagrams, labs, readings.
Week 2
AI Pair Programming from Amazon Q to Prompt Engineering
- Prompt Engineering
  Learn the art of crafting effective prompts for AI models. Explore workflows, summarization techniques, and practical applications in coding. Develop skills to optimize AI interactions and enhance output quality.
- Getting Started with Amazon Q Developer
  Dive into AI-assisted coding with Amazon's CodeWhisper. Explore its application in Rust development and understand how Large Language Models (LLMs) enhance programming workflows.
- Amazon Q Developer for the Command-Line
  Master Amazon Q Developer's command-line capabilities. Learn to install, configure, and use CodeWhisperer CLI. Explore building Bash functions and CLIs with AI assistance.
Week 3
Amazon Bedrock
- What is Amazon Bedrock?
  Explore Amazon Bedrock, a fully managed service for building generative AI applications. Learn about its key components and features. Understand how Bedrock enables easy integration of foundation models into applications.
- Getting Started with the Bedrock SDK
  Dive into Amazon Bedrock SDK implementation. Explore both Python (Boto3) and Rust (Cargo) SDKs for Bedrock. Learn to invoke and list models using these SDKs. Gain practical skills in integrating Bedrock's AI capabilities.
- Invoking Bedrock via the SDK
  Master the process of invoking Amazon Bedrock through its SDK. Focus on using the Bedrock Runtime SDK and implementing it in Python with Boto3.
Week 4
Project Challenges
- Challenge - From Rust Cargo Lambda to Bedrock Agents
  Tackle an advanced challenge integrating Rust Cargo Lambda with Amazon Bedrock Agents. Through readings, videos, and hands-on lab work, learn to build sophisticated AI-powered applications.