Deepnote is a new kind of data notebook that’s built for collaboration — Jupyter compatible, works magically in the cloud, and sharing is as easy as sending a link.
Amazon SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML.
Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. There's a number of vendors offering Jupyter notebooks as a managed service.
Collaborate across engineering, data science, and machine learning teams with support for multiple languages, built-in data visualizations, automatic versioning, and operationalization with jobs.
A powerful online environment for Jupyter notebooks. Use smart coding assistance for Python in online Jupyter notebooks, run code on powerful CPUs and GPUs, collaborate in real-time, and easily share the results.
Runs anything you can put into a Docker container. Improve your workflow with polyglot notebooks, automatic versioning and real-time collaboration. Save time and money with on-demand provisioning, including GPU support.
Make sense of the world with data, together. Explore, visualize, and analyze data. Collaborate with the community. Learn and be inspired. Share insights with the world.
Visual Studio Code is a lightweight but powerful source code editor. It supports working with Jupyter Notebooks natively, as well as through Python code files.
Polynote is a different kind of notebook. It supports mixing multiple languages in one notebook, and sharing data between them seamlessly. It encourages reproducible notebooks with its immutable data model.