I use Zepl for collaborative data science work, and I really like having a managed environment where the team can share notebooks, code, visualizations, and results without dealing with much infrastructure. The built-in code assistance speeds up routine development. I appreciate having a shared notebook environment where the team can experiment with data and see results without constantly switching between tools. The collaboration and visualization capabilities make exploratory work smoother, and the flexible notebook setup is valuable for prototyping. Real-time collaboration is especially useful during experiments or debugging, as everyone can see changes and results in one place. Additionally, the initial setup of Zepl was very easy. Review collected by and hosted on G2.com.

One thing I’d like to see improved is the transition from exploratory notebooks to more production-oriented ML workflows, since teams can still need additional tools for things like feature management, pipeline orchestration, and deployment. Zepl is strong for collaborative analysis and experimentation, but adding more built-in MLOps capabilities and deeper integrations would make it more useful across the full lifecycle of a project. Review collected by and hosted on G2.com.