ML ENGINEERING AS A SERVICE
We get your data scientists the data they need.
The bulk of the work (upto 80%) for most data science teams is feature engineering - the laborious, error-prone wrangling of data, into the finely tuned features or variables that best train each ML model.
Scribble takes raw data (from a data lake or other store), and our output is versioned feature matrices, designed to be scalable, evolvable, and run at any frequency, all of this built atop our platform, for each customer.
Scribble helps data scientists figure out where the right graft and intervention needs to be made in the layers of their code and data, so that iterative improvements can be made quickly.
WHAT SCRIBBLE DATA
The best ML teams understand what it takes to build living, breathing ML models.
They know what it takes to tend to them as they grow, do well for themselves and the organization, and then grow old, against the evolving landscape of the business usecases for which they were built. They understand also what it means to birth and nurture multiple such models simultaneously, and how to do so in a way that the models are robust, and their time-to-market is as quick as the business needs themselves.
A lot of this comes down to feature engineering - getting the data flowing into these models, whether for training or when deployed, right.
It also means thinking through the reusability of features, in a trusted marketplace with the organization. And it means building in checks and balances to monitor the performance of these models in production.
All of this is key to the ML engineering work that Scribble does, and to how we’ve built Enrich.