Einblick Is a “No Unnecessary Code” Platform
Think about how much productive data science time has been lost to making graphs with ggplot or matplotlib! And how tedious it can be to build a data cleaning workflow.
Instead, Einblick Is a “No Unnecessary Code” platform. Within Einblick, there is no trade-off between ease of use and functionality limitations. Coding is also first class, and chunks of code can be written directly into a Python notebook operator. The goal is to create hybrid code / no-code workflows, where there exists no data science workflow that is harder within Einblick than in a notebook, and only a number of operations which are much easier.
Our Computation Engine is Faster Because We Work Smarter, Not Harder
Einblick allows the user to operate in near-real time, whether a dataset is a few rows or a few terabytes. For every output — from histograms to custom Python operations to ML model runs — you will be able to start drawing inference without delays due to machine time.
This is made possible by Einblick’s proprietary computation engine, Davos. Einblick takes samples of the underlying data to return an initial estimate within a second. Then, in the background, the engine will progressively compute over larger and larger batches until completion.
So ultimately, even when stripping away all the no-code functionality, it still makes sense to execute scripts on Einblick’s progressive engine instead of traditionally computing results within a notebook-based application or trying to manually sample and then running into roadblocks when scaling up.
Working Together Works Better
We all work better when collaboration is verbal, visual, and in real time. For instance:
- Don’t text to break up.
- A quick call solves problems faster than email.
- A whiteboard session is a great way to brainstorm.
- Figma has taken the design world by storm.
- You should FaceTime your mother.
When it comes to data science, we have a long way to go. In our space, work tends to be performed asynchronously and discussions are based on PowerPoint and emails. That’s why Einblick became the first realtime canvas to bring your team together into a single workspace, and work on problems together.
Just take modeling – while our AutoML tool may easily generate accurate models, model creation is just a narrow slice of the analytic process. No modeling tool replaces context gathering or disseminating answers, which can end up being significantly harder than the analysis itself. But given a coworking canvas, you can start a new project with key stakeholders live in the workspace with you, and they can directly participate in no-code descriptive analysis and collaborate on data prep. And then, after models are created, they can directly interact with your explainability visualizations and ask questions relevant to the data.