The hedge fund wanted to maximize the value of its research efforts over time.
It needed a way to help members of its data science team work together more effectively in a remote environment and regularly build on each other’s work.
With Deepnote — which allows the hedge fund to access high-memory machines and GPUs on demand — the team can easily collaborate in real time or asynchronously, all while improving the organization and discoverability of projects.
The data science team often works in small groups, with team members coding together in real time while sharing the same execution environment. Projects can then be shared as a link, allowing teammates to jump into each other’s notebooks and comment with questions and feedback. The commenting feature helps the team streamline project hand-offs and eliminate the unnecessary duplication of work.
Notebooks, scripts, and other relevant assets are stored and organized in workspaces where team members can easily search, access, replicate, and build on the work of their teammates. Granular permissions help the team manage who can access which projects and at what level (viewer, editor, etc.). Meanwhile, version history makes it easy for team members to revert to past states of notebooks and easily reproduce their teammates’ work.
Instead of copying and pasting code from a locally hosted notebook to a code editor and back again, team members can easily write clean, reproducible code in the same medium they use for collaboration. Built-in code intelligence features — including linting, debugging, and autocomplete — allow the team to develop directly in their notebooks and iterate rapidly.
The hedge fund’s data science team can now collaborate much more quickly and easily, helping it speed up time to insight. With the ability to code together, comment on each other’s work, share notebooks via link, and effortlessly access existing projects, the organization can focus less on hardware hosting, envrionment configuration, and notebook management in favor of serving investors.