In a previous post on data maturity, we discussed a company that was just embarking on a transformation: launching a new services business and building data capabilities to support that business. But what if you’re not starting from the beginning?
In this post we look at how to visualize data gaps, and engage senior leadership.
No two situations are the same, but we have found one truism: making a data transformation successful requires much more than simply getting the technology right.
In this interview, we talk about extending the concept of interoperability between multiple libraries in Python into other programming languages, and the pain points this will address.
In this post, we will discuss what “real” gaps in data look like and how to find them in your organization.
A quick overview of the motivation behind our instant and repeatable data platform tool.
In this post, we’ll cut through some of the ambiguity around IoT applications, and introduce an example data science problem relevant to the IoT world.
The DeepGramAI Hackathon has concluded, check out the project that Data Engineer Matthew Rubashkin worked on.
In this post, we’ll provide a short tutorial for training a RNN for speech recognition; we’re including code snippets throughout, and an accompanying GitHub repository. The software we’re using is a mix of borrowed and inspired code from existing open source projects.