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Get a Telegram notification when your model finishes training on Deepnote.
We built Deepnote because analysts and data scientists don’t work alone.
Click on a link to jump into any project and see in real-time what everyone is up to.
No more out-of-sync files.
“Working in Deepnote is like code-review and rapid prototyping at the same time, saving valuable time in the iteration cycles.”
No need to email files or take screenshots of charts. In Deepnote, you can share projects by sending a link to anyone. By setting team permissions, you decide who can edit code and who can view it.
Discuss and debug in real-time by commenting on code and visualizations. No more email or Slack messages to get feedback on your work.
Are you looking at the latest version of your file? See all changes as they happen when you are working with colleagues or clients.
We created Deepnote to help you save time. Whether you work in a team or by yourself, Deepnote helps you clean your data, write complex queries, build predictive models, and ship beautiful apps.
Don’t jump between multiple apps. Query data from wherever it’s stored, including Snowflake, BigQuery, Redshift and PostgreSQL. Switch between SQL and Python in order to transform, clean, and export your data.
Rapidly generate interactive charts to discover patterns in your data. No need to fuss with visualization libraries. Simply pick the data you want to plot, and Deepnote will do the rest.
No more broken pip installs or mismatched requirements. Ensure reproducibility across your team by re-using the same environments throughout your projects. Create custom environments within Deepnote or import existing ones from Dockerhub, GCR, and ECR.
Develop faster with intelligent autocomplete and linting tools. Deepnote points out bugs before they break long training jobs, just like you are used to from a local IDE such as VSCode, PyCharm or Sublime Text. The table names and columns are auto-suggested when writing SQL.
Deepnote integrates flawlessly with all your existing infrastructure and processes.
Use Python, R, Julia, TensorFlow, PyTorch, or any of your favorite languages or frameworks.
We’ve also pre-built 100s of native integrations that simplify the process of connecting your data sources.
All integrations are encrypted and can be easily shared with your team. No exposed passwords in your notebooks.
Attach a repository to your project to read and commit to any branch.
Mount Google Drive directories in your projects to read and edit its files.
Connect to your BigQuery warehouse and query the data with dedicated SQL cells.
Connect to a Snowflake warehouse and query the data with dedicated SQL cells.
Attach a bucket to your project to read, edit and upload files to the bucket.
Connect to a Postgres instance and use SQL directly from a notebook inerface.
Secure by default, Deepnote follows industry best practices, including fine-grained access controls, SSO support and on-premise deployments.
Deepnote is the favorite tool for thousands of data scientists all over the world.
Showcase your projects, join the discussion on best data science practices, or simply learn faster thanks to our community.
Our community forums are the best place to get started and get answers to your Deepnote and data science questions.
Whether it’s machine learning, visualizations, working with pandas or posting memes, we've got you covered.
Your feedback is what ultimately shapes Deepnote. Our community is the best place to give us feedback on new features you would like to see.
Awesome service for working on Jupyter Notebooks collaboratively. My students are currently using it for their machine learning class projects; and it's also useful for me to check & comment. Really cool is the data management. No mounting required, it's just drag and drop.
Delightful user experience reminds me of Superhuman with the command palette and constant reminders of how to use hotkeys to work more efficiently.
Machine learning is a very empirical discipline so iteration speed is everything - working in Deepnote is like code-review and rapid prototyping at the same time, saving valuable time in the iteration cycles.