A new kind of data science notebook
If you are a data scientist like me, you probably spend more time on engineering tasks rather than on actual research. Installing libraries, managing databases, keeping track of your experiments, debugging code, running out of memory… You get the idea.
Why is that? Let’s be honest: data science is hard. And the tools we use are not as helpful as they could be. Most of these tools were made by and for software engineers. But our work is different.
We explore data, we search for patterns, we discover outliers, we train models that help us make faster and better decisions. That’s what we’re good at. But it’s difficult to do while also being an engineer and thinking about how to write beautiful and scalable code that will go straight into production.
We need tools that are both powerful and easy to use. Tools that make us more efficient and don’t stand in our way. Tools that help us collaborate and share our findings. Tools that scale as our teams grow.
That’s why we’re building Deepnote.