Amazon SageMaker Studio Lab continues to democratize ML with more scale and functionality
To make machine learning (ML) more accessible, Amazon launched Amazon SageMaker Studio Lab at AWS re:Invent 2021. Today, tens of thousands of customers use it every day to learn and experiment with ML for free. We made it simple to get started with just an email address, without the need for installs, setups, credit cards, or an AWS account.
SageMaker Studio Lab resonates with customers who want to learn in either an informal or formal setting, as indicated by a recent survey that suggests 49% of our current customer base is learning on their own, whereas 21% is taking a formal ML class. Higher learning institutions have started to adopt it, because it helps them teach ML fundamentals beyond the notebook, like environment and resource management, which are critical areas for successful ML projects. Enterprise partners like Hugging Face, Snowflake, and Roboflow are using SageMaker Studio Lab to showcase their own ML capabilities.
In this post, we discuss new features in SageMaker Studio Lab, and share some customer success stories.
New features in SageMaker Studio Lab
We have continued to develop new features and mechanisms to delight, protect, and enable our ML community. Here are the latest enhancements:
- To safeguard the CPU and GPU capacity from potential usage abuse, we launched a 2-step verification, increasing the size of the community we can serve. Going forward every customer be required to link their account to a mobile phone number.
- In October 2022, we rolled out automated account approvals, enabling you to get a SageMaker Studio Lab account in less than a day.
- We tripled capacity for GPU and CPU, enabling most of our customers to get an instance when they need it.
- A safe mode was introduced to help you move forward if your environment becomes unstable. Although this is rare, it typically happens when customers exceed their storage limits.
- We’ve added support for the Juptyer-LSP (Language Server Protocol) extension, providing you with code completion functionality. Note that if you got your account before November 2022, you can get this functionality by following few simple instructions (see FAQ for details).
Customer success stories
We continue to be customer obsessed, offering important features to customers based on their feedback. Here are some highlights from key institutions and partners:
“SageMaker Studio Lab solves a real problem in the classroom in that it provides an industrial-strength hosted Jupyter solution with GPU that goes beyond just a hosted notebook alone. The ability to add packages, configure an environment, and open a terminal has opened up many new learning opportunities for students. Finally, fine-tuning Hugging Face models with powerful GPUs has been an amazing emerging workflow to present to students. LLMs (large language models) are the future of AI, and SageMaker Studio Lab has enabled me to teach the future of AI.”
—Noah Gift, Executive in Residence at Duke MIDS (Data Science)
“SageMaker Studio Lab has been used by my team since it was in beta because of its powerful experience for ML developers. It effortlessly integrates with Snowpark, Snowflake’s developer framework, to provide an easy-to-get-started notebook interface for Snowflake Python developers. I’ve used it for multiple demos with customers and partners, and the response has been overwhelmingly favorable.”
—Eda Johnson, Partner Industry Solutions Manager at Snowflake
“Roboflow empowers developers to build their own computer vision applications, no matter their skillset or experience. With SageMaker Studio Lab, our large community of computer vision developers can access our models and data in an environment that closely resembles a local JupyterLab, which is what they are most accustomed to. The persistent storage of SageMaker Studio Lab is a game changer, because you don’t need to start from the beginning for each user session. SageMaker Studio Lab has personally become my go-to notebook platform of choice.”
—Mark McQuade, Field Engineering at Roboflow
“RPI owns one of the most powerful super computers in the world, but it (AiMOS) has a steep learning curve. We needed a way for our students to get started effectively, and frugally. SageMaker Studio Lab’s intuitive interface enabled our students to get started quickly, and provided powerful GPU, enabling them to work with complex deep learning models for their capstone projects.”
—Mohammed J. Zaki, Professor of Computer Science at Rensselaer Polytechnic Institute
“I use SageMaker Studio Lab in basic machine learning and Python-related courses that are designed to give students a solid foundation in many cloud technologies. Studio Lab enables our students to get hands-on experience with real-world data science projects, without them having to get bogged down in setups or configurations. Unlike other vendors, it is a Linux machine for students, and students can do much more coding exercises indeed!”
—Cyrus Wong, Senior Lecturer, Higher Diploma in Cloud and Data Centre Administration at the Department of Information Technology, IVE (LWL)
“Students in Northwestern Engineering’s Master of Science in Artificial Intelligence (MSAI) program were given a quick tour of SageMaker Studio Lab before using it in a 5-hour hackathon to apply what they learned to a real-world situation. We expected the students to naturally hit some obstacles during the very short time period. Instead, the students exceeded our expectations by not only completing all the projects but also giving very good presentations in which they showcased fascinating solutions to important real-world problems.”
—Mohammed Alam, Deputy Director of the MSAI program at Northwestern University
Get started with SageMaker Studio Lab
SageMaker Studio Lab is a great entry point for anyone interested in learning more about ML and data science. Amazon continues to invest in this free service, as well as other training assets and scholarship programs, to make ML accessible to all.
Get started with SageMaker Studio Lab today!
About the author
Michele Monclova is a principal product manager at AWS on the SageMaker team. She is a native New Yorker and Silicon Valley veteran. She is passionate about innovations that improve our quality of life.
Shared by: AWS Machine Learning
November 11, 2022
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