Paperspace is doing a great job providing infrastructure for data science workloads and mlops. The target users are data scientists/engineers. The ability to share with non-technical users is quite limited.
We built Deepnote so that the work you do as a data scientist can be shared with both engineers and non-technical folks. We're not really an mlops platform. We make a really good notebook that integrates with other platforms.
Thanks for the reply. To clarify, I was referring to their Gradient Notebook specifically [1], which seem to have feature parity and have the additional benefit of vertical integration.
https://gradient.paperspace.com/notebooks
Got it. I can't speak of Gradient's roadmap, but as of right now they are using Jupyter as a notebook and focusing on infrastructure around it. We are innovating on the notebook itself.
Huge part of it is simply the UX. There's a wide range of what kind of work a data scientist does. Some train models that go into production, some analyze the datasets and build reports. Probably best to try both products with your workload and see what works better.
We built Deepnote so that the work you do as a data scientist can be shared with both engineers and non-technical folks. We're not really an mlops platform. We make a really good notebook that integrates with other platforms.