Atop Scribble Enrich, our ML Engineering Platform
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We help your data science teams get 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.


Understand Data Use

Understanding the datasets that are in production, in the context of the business problem that needs to be solved, and surfacing summaries to data scientists



Implementing pipelines to generate the continuously computed datasets that ML models need to be trained on, and fed with when put into production.



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For Retail Stores

Are you a retail store trying to scale your growth? The Enrich platform computes attributes for a number of core entities such as store, customer and SKU, to continuously compute rich profiles at the granularity of each individual entry. This enables near real-time decision making on actions like assortment, new store locations, customer engagement through marketing and offers, and more.

Whatever were we thinking?

The best ML teams understand what it takes to build living, breathing ML models, 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.