The Roboflow Notebooks GitHub repo contains over 20 open source computer vision notebooks with step-by-step guides on using 13 different computer vision model architectures. Along with ready-to-use notebooks, the repository includes blog posts, YouTube videos, and GitHub repositories to help software developers, machine learning engineers, and computer vision engineers explore state-of-the-art computer vision model architectures, immediately usable for training with custom datasets.
With 100,000+ developers and over half the fortune 100 using Roboflow, many of our users rely on AWS as their cloud platform of choice and our goal is to make building an integrated machine learning operations pipeline with AWS as easy as possible. Starting today, we are expanding support for notebooks in Amazon SageMaker Studio Lab, including YOLOv5, YOLOv7, Stable Diffusion and more.
Amazon SageMaker Studio Lab is a free machine learning development environment that provides compute, storage (up to 15GB), and security at no cost to learn and experiment with machine learning. There is no infrastructure to configure and you can get started without an AWS account. SageMaker Studio Lab accelerates model building through GitHub integration, and it comes preconfigured with the most popular ML tools, frameworks, and libraries to get you started immediately.
SageMaker Studio Lab just announced the capability to take development notebooks written in SageMaker Studio Lab, and schedule them to move into your AWS account managed by full Amazon SageMaker. This unique capability of Studio Lab lifts the limits of the types and sizes of projects you can achieve, providing access to the full suite of Amazon SageMaker tools, including Amazon SageMaker Studio, Amazon Sagemaker Experiments, and Amazon SageMaker Autopilot.
“The Roboflow Notebooks repository provides tutorials and datasets to teach developers how to leverage computer vision models,” said Mark McQuade, head of Partnerships and Field Engineering for Roboflow. “The notebooks can run anywhere, but the new capability to automate notebooks as jobs makes SageMaker Studio Lab compelling. Now customers can scale their notebooks by launching them in their AWS account with any instance type without changing a single line of code.”
Moving from a notebook into production increases the productivity of software developers, machine learning engineers, data scientists, and computer vision engineers. When it comes to equipping highly skilled experts with tools to do their jobs, every minute of increased productivity is extremely valuable and we are excited to support our community to adopt the newest advancements in ML technology.
Learn more about how you can use Roboflow and AWS together by getting in touch with our team today.