Data science startup Deepnote has raised USD3.8 million to improve its cloud-based platform that lets data scientists do their work without the tedium of setting up the requisite infrastructure themselves.
Founded just last year, the platform is popular for how it enables easy collaboration, gaining thousands of users from the data science community. The funding is expected to go into further developing Deepnote, including enhancing its overall user experience.
Tool for data science
With the increasing importance of data science, data scientists are working in large teams, and increasingly expected to do more than focus on pure research. This means day-to-day work can be expedited with the ability to collaborate and share code between team members.
And with the need to integrate the resulting work into new products and capabilities, code reuse and properly tracking various iterations of code with version control become crucial to the success of a project.
Built around Jupyter notebooks, Deepnote offers a popular IDE-like environment for real-time collaboration to quickly develop machine learning models. The platform offers versioning, code review and easy reusability of algorithms, and is popular among data scientists who need to collaborate or switch between languages.
According to co-founder and CEO Jakub Jurovych, Deepnote is highly popular as an educational tool. Teachers would use the Deepnote platform to publish interactive exercises that their students can access and work on.
“We understand the needs of a data scientist or a machine learning expert are different to those of a software engineer, and we want to bring the ideas found in the best software engineering tooling to data science while preserving the unique workflow that data science has,” explained Jurovych in a blog post.
Despite the ability of users to work with large data sets and train models using cloud-based machines with GPUs, Deepnote does not currently charge for its service.
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