Vincent Tang

9 December 2020

2 min read

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Data is getting more complex, datasets are becoming bigger than ever, and machine learning (ML) techniques are getting more sophisticated with time. Unfortunately, for businesses to make sense of all that data they must rely on advanced algorithms, and the sheer size of today’s data forces users to interact with it through custom jobs written in scripting or programming languages.

To help solve these problems and enable more enterprises to make better use of the data they have, Samsung Next portfolio company Einblick has built a visually interactive data collaboration environment that data scientists and analysts can use to understand the meaning hidden in their data. Today, Einblick announced it has raised $6 million in seed funding, and we are proud to call ourselves investors alongside Amplify Partners and Flybridge.

Einblick enables automated machine learning (“AutoML”) for enterprises. For businesses without sophisticated data science teams, Einblick’s technology allows subject matter experts to create their own empirical models. And for those who have already invested in data science, Einblick’s tech will increase the productivity of those data scientists through automation.

Einblick’s primary innovation is automatically finding the best possible model for a specified task. Based on four years of research in MIT’s Data Science and AI Lab (DSAIL), the technology can automatically infer task type (e.g. classification, time series forecasting) and algorithm type (e.g. decision tree, bayesian linear regression) directly from a data set and then, using AI-search techniques, identify the best possible model out of a big space of possible models.

We believe AutoML will be a major opportunity in the cloud computing space, as it will generate incremental demand for compute and storage, and abstract away expensive things like data warehousing and data pipelines.

But AutoML without an intuitive interface for exploratory data analysis amounts to monkeys banging away on typewriters in a data center, running up cloud computing bills. That’s because the space of possible models is really, really big: there are an infinite number of combinations of model architectures, each wandering around in boundless parameter space, often without helpful gradients to guide them.

We believe AutoML will be a major opportunity in the cloud computing space, as it will generate incremental demand for compute and storage, and abstract away expensive things like data warehousing and data pipelines.

That’s where Einblick comes in: In short, it enables companies to gain insights more quickly through an incredibly powerful and intuitive drag-and-drop data analytics tool for collaborative data exploration and automated model building.

Einblick’s technology fits directly into current established workflows, and can visualize data from a variety of systems, ranging from various database installations to distributed file systems that hold most of an enterprise’s data.

While we believe the technology will unlock significant opportunities for enterprises around the globe, we were equally impressed by the team, which is made up of computer scientists from MIT and Brown University who have published more than 20 peer-reviewed publications on the core technology behind Einblick.

We also believe that they bring to the table a key technical advantage that will translate into meaningful product differentiation over time. For instance, in a recent DARPA competition around automated machine learning (“D3M”), its technology blew the competition out of the water. It was the only team that solved all of the DARPA-provided tasks, outperforming expert solutions in 80% of the time (out of 300 tasks).

For all these reasons, we’re excited to be investors in Einblick as they bring this exciting technology to market.

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