Objectiv now supports Deepnote: collaborate on notebooks that are powered by Objectiv’s high quality product analytics data and pre-built models.
At Objectiv, we’ve been fans of Deepnote for a while and have been using it internally to collaborate on notebooks. We’re happy to announce Objectiv now has built-in support for Deepnote.
What this means:
- All example notebooks (that can be used as a boilerplate for your own analyses) can be run in Deepnote directly without additional setup.
- Full support for all pre-built models & functions in the open model hub.
- Instructions in our documentation on how to use Deepnote with Objectiv.
All our example notebooks will work directly with Deepnote. Just get them from our repo and import them into Deepnote.
Here is a retention analysis example from our basic product analytics notebook, running as an app in Deepnote:
Retention analysis, powered by Objectiv. Click to open in Deepnote.
But you can also use it for more advanced use cases, i.e. logistic regression modeling to predict user behavior:
Predicting user behavior with logistic regression. Click to open in Deepnote.
There is a lot more
These are just a handful of examples, but you can use Deepnote to run any model from the open model hub directly on your full data warehouse.
How to get started
If you are already using Deepnote, follow the instructions to get started. If you don’t have access to Deepnote yet, you can first sign up for a free account here.
If you have any questions about this release or anything else, or if you just want to say 'Hi!' to team Objectiv, we have Office Hours every Thursday at 4pm CET, 10am EST that you can freely dial in to. If you're in a timezone that doesn’t fit well, just ping us on Slack and we'll send over an invite for a better moment.