Amazon SageMaker Studio Lab helps educators focus on teaching rather than technology
In the modern workplace, the ability to program and understand how to apply machine learning (ML) is an increasingly important skill. A World Economic Forum report estimates that 50% of all employees will need reskilling by 2025 as adoption of technology increases. Programming is no longer a niche skill but a core foundational requirement for many of these roles. It’s important that the technology and methods educators use to teach these important skills aren’t barriers to learning.
The browser-based computational notebook tool, Jupyter, provides students and educators with an interactive learning environment to accelerate programming learning. But setting up collaborative Jupyter notebooks at the classroom and institutional level can be time-consuming and costly. Amazon SageMaker Studio Lab from Amazon Web Services (AWS) is a no-cost service built on Jupyter notebooks that takes care of the configuration and security of setting up multi-user Jupyter notebook environments – so educators can focus on teaching and learners can accelerate their journey in ML.
Traditional introductions to programming can produce barriers to learning
In order to start learning about ML, students must first learn about programming. Learning outcomes are heavily influenced by technology choices such as programming environment and language. In the traditional approach to teaching programming, students are first introduced to the command line. Often, before getting to code, they have to learn how to navigate in Linux. Once they’re comfortable navigating and running applications, they learn to start an editor. Only then can they write, compile their code, and finally run a program. In a traditional programming teaching model, writing the standard first program to print ‘Hello World!’ on a computer screen can sometimes fill a three-hour lab.
If programming is supposed to be useful, why does it have to involve learning so many different technologies to get started? Pedagogically, it is far from ideal for learners to invest this much time before they begin to realise any tangible benefit. Educators want to be able demonstrate the value, the “Why are we doing this?” for students as early as possible.
Interactive teaching environments can accelerate programming learning
Jupyter is an open-source, browser-based computational notebook platform that provides an interactive coding environment that can support dozens of coding languages. The web interface provides an intuitive experience and the interactive environment offers more potential for learning and teaching. The platform interface includes markdown cells alongside code so students can document their learning in the same place as their coding project. Jupyter notebooks are so popular because they provide seamless integration for teaching and describing methods or research.
For educators and learners, starting a programming course by actually writing code makes a difference. Students can immediately see the result. Plus, Jupyter notebooks offer much more. When someone makes an error, it becomes a learning opportunity—rather than quickly brushing over a mistake with a fix, as in a linear code line, Jupyter notebooks allow the coding mistakes and their outputs to be preserved. The correction can be made in the next code cell, where the student can record their learning experience. Student questions can be explored in code in real time. Interactive, collaborative environments make teaching more transparent and accessible for learners.
As with any technology, notebooks need to be used appropriately, with content and teaching tailored for the opportunities they provide learners. It is possible to deliver the same traditional introductory programming courses on Jupyter notebooks. But with Jupyter, content can be ordered to make learning more accessible and constructive. An interactive web platform can turn every lecture room into a digital lab.
Amazon SageMaker Studio Lab saves educators time and creates student opportunities
Jupyter notebooks can be run on personal devices which require installation and configuration. But configuring this in class takes time away from teaching, resolving issues with different devices. An alternative is to use JupyterHub, a multi-user platform that runs Jupyter notebooks on a web server rather than on devices. JupyterHub can be pre-configured with the necessary libraries for courses, so students have the same environment, giving a consistent experience for learners and educators.
The Jupyter community provides single instance and container-based solutions and deployment guides like Littlest JupyterHub and Zero to JupyterHub. But these deployment frameworks still need to be configured, debugged, managed, and integrated with systems for governance and security compliance – tasks many educators and educational institutions don’t have the resources to manage. In addition, there is the cost of running the platform. This may be modest for a single course or department, but becomes significant at the institutional scale.
This is where Amazon SageMaker Studio Lab comes in. At no cost, SageMaker Studio Lab provides the compute, storage, and security infrastructure for anyone to learn and experiment with machine learning in an interactive development environment. SageMaker Studio Lab is built on the JupyterLab environment, so educators can use existing materials in Jupyter notebooks. With SageMaker Studio Lab, educators can focus on content creation and teaching without having to manage infrastructure or install and troubleshoot software. Students focus on learning to program, tools for good practice, and beginning their ML journey.
Professor Nick Hawes at the University of Oxford teaches disadvantaged pupils programming as part of OxNet, an outreach program run by Pembroke College. “I contacted the team at AWS to explore how I could run JupyterHub for the outreach programme. It was a huge relief when they suggested SageMaker Studio Lab. I didn’t have to worry about servers or configuring the software or getting Jupyter set up on the pupil’s own devices. It just worked. And the students keep those accounts so they can explore more and prepare themselves better for applying to university. It saved me some time, but it could change their lives.”
Get started with Amazon SageMaker Studio Lab
In SageMaker Studio Lab, users can select from CPU backed environment (12 hour sessions) or GPU (4 hours session) with up to 15GB of storage. Students and educators can request a SageMaker Studio Lab account here. No credit card is required, and the service does not require an AWS account. You can find out more in this introductory blog post. Check out this curated repository of example content as the SageMaker Studio Lab community of educators who want to contribute continues to grow.
SageMaker Studio Lab is currently in preview and needs an email to sign-up. Happy learning.
Learn more about how you can use AWS to innovate and benefit the student experience at the AWS for Education hub. Do you have more questions about how cloud technology can support your institution? Reach out to the AWS Public Sector Team.
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