Build hands-on skills with 3 AI projects on AWS for free
In this article, I will introduce you to 3 free ways to build AI projects on AWS, suitable for beginners to pros, with tutorials to use free environments.
Viktoria Semaan
Amazon Employee
Published Jun 5, 2024
Last Modified Jun 6, 2024
Are you interested in gaining hands-on experience with AI but worried about possible charges on your AWS account or blocked from opening one due to the credit card requirement?
👋 Hi! My name is Viktoria, and I am a Senior Developer Advocate passionate about helping builders from different backgrounds develop technical skills and advance in their career journeys.
In this article, I will walk through 3 free ways to build AI projects on AWS, suitable for beginners to pros along with links to tutorials that allow you to use free environments. These hands-on environments are completely free and require only a personal email to sign up.
As AI becomes more integrated into our daily lives and business operations, the demand for skilled prompt engineers is skyrocketing. LinkedIn reports a 250% increase in job postings for roles related to prompt engineering and AI interaction design in the last year alone.
So, how can you gain in-demand AI skills and avoid the risk of incurring costly charges? Let’s dive in!
AWS Educate is a self paced online training program open for anyone interested in gaining technical experience with cloud computing services. Learners as young as 13 can register for AWS Educate with just an email address, no credit card is needed. It provides access to over 18 hands-on labs with the simulated AWS console to practice.
In addition, you can earn digital badges upon completion, which can be added to your LinkedIn profile to demonstrate you skills to potential employers.
Once registered, you can access the AWS Educate Job Board to explore, search for, and apply to thousands of in-demand jobs and internships with organizations of all types all over the world.
➡️ Register here to begin your journey with AWS Educate.
- Gain Hands-On Skills: Access 18 labs with simulated AWS Console environment.
- Earn Digital Badges: Achieve up to 11 shareable badges to showcase your knowledge.
- Create and Share Your Portfolio: Build and share a career portfolio.
- Explore Job Opportunities: Utilize the AWS Educate Job Board to find and apply for jobs.
Start by learning AI fundamentals with the “Introduction to Generative AI” course. In this course, you will explore the differences between AI and Generative AI, understand various foundation model types, delve into prompt engineering, and discover use cases for generative AI.
Then, progress to hands-on labs such as “Machine Learning Foundations,” where you will practice building machine learning projects in a hands-on lab environment that teaches you how to navigate the AWS Console and get introduced to Amazon SageMaker.
PartyRock simplifies the process of building AI applications with a no-coding environment. This platform is perfect for experimenting with prompt engineering techniques, rapid prototyping of AI-powered applications, and gaining experience in generative AI. PartyRock provides builders with access to foundation models from Amazon Bedrock, including Claude 3 models, Amazon Titan, Jurassic-2, and others.
➡️ To get started visit partyrock.aws, sign in using your email address, and click “Build your own app.”
- Develop Prompt Engineering Skills: Practice creating effective prompts for AI models.
- Experiment with Various Models: Work with diverse AI models from Amazon and other companies.
- Learn Generative AI Basics: Gain hands-on experience with AI fundamentals without needing to code.
- Rapid Prototyping: Quickly prototype and test AI app ideas.
- Build Practical Apps: Create useful tools like recipe study planners, quizzes, and more.
I have built several AI-powered applications to assist with various tasks. For example, my text-to-SQL app that converts natural language into SQL queries, and the resume optimizer app that analyzes resumes and provides tips to tailor them to specific job descriptions. Feel free to try them out!
You can find my applications and a detailed tutorial on how to build AI-powered apps without coding using PartyRock in this tutorial I shared on community.aws
Amazon SageMaker Studio Lab is a free service that gives customers access to AWS compute resources, in an environment based on open-source JupyterLab. It is based on the same architecture and user interface as Amazon SageMaker Studio Classic, but with a subset of Studio Classic capabilities.
With Studio Lab, you can use AWS compute resources to create and run your Jupyter notebooks without signing up for an AWS account. Because Studio Lab is based on open-source JupyterLab, you can take advantage of open-source Jupyter extensions to run your Jupyter notebooks. You can select CPU or GPU compute to train your ML models.
With Studio Lab, you can use AWS compute resources to create and run your Jupyter notebooks without signing up for an AWS account. Because Studio Lab is based on open-source JupyterLab, you can take advantage of open-source Jupyter extensions to run your Jupyter notebooks. You can select CPU or GPU compute to train your ML models.
Amazon SageMaker Studio Lab is absolutely free, no credit card or AWS account required. This service is an excellent way to get familiar with the practical aspects of ML without incurring costs.
➡️To get started visit: studiolab.sagemaker.aws
- Hands-On Machine Learning Skills: Gain practical experience by working on Jupyter notebooks in a no-setup environment.
- Experiment with CPU and GPU Computing: Understand the differences and applications of CPU and GPU runtimes for machine learning projects.
- Develop and Train ML Models: Practice building, training, and deploying machine learning models using popular libraries like Pandas and Scikit-learn.
- Integrate with Git: Learn to manage and collaborate on code with Git integration, including cloning repositories and version control.
I created a tutorial on how to build a ChatGPT-Powered AI tool to learn technical things fast
In this step-by-step guide you will learn how to set up a free ML environment using SageMaker Studio Lab, leverage Large Language Models (LLMs) and ChatGPT APIs to extract insights from YouTube videos, and empower yourself to learn faster and more efficiently.
In this step-by-step guide you will learn how to set up a free ML environment using SageMaker Studio Lab, leverage Large Language Models (LLMs) and ChatGPT APIs to extract insights from YouTube videos, and empower yourself to learn faster and more efficiently.
Additionally, explore example notebooks for working with SageMaker Studio Lab, saved in the example repository.
By completing these 3 challenges, you'll gain a fundamental understanding of AI and ML, learn how to build AI-powered apps, and understand how to deploy and train models:
- AWS Educate Labs: Get hands-on machine learning experience with simulated AWS Console.
- PartyRock Playground: Build AI-powered apps without coding in a no-code environment.
- SageMaker Studio Lab: Work on Jupyter-based ML projects with free compute resources.
Who’s up for this hands-on AI challenge? Let me know in the comments! 🤗
Any opinions in this post are those of the individual author and may not reflect the opinions of AWS.
6 Comments
Log in to comment