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 enables data scientists to make the right grafts and interventions in the layers of their code and data, so that iterative improvements can be made quickly across the machine learning model deployment pipeline.


We Help You 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


We Implement Your Data Pipelines

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

What Scribble Data Can Do


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.


For Shopping Malls

Shopping malls can use Enrich to understand the shopping paths and behaviors of shoppers, by continuously ingesting WiFi data. This data is used to compute attributes such as visit frequency, brand affinity and shopping paths, and helps enhance the individual shopper's experience with customized offers, as well as attributes revenue to various such initiatives. 


For eCommerce 

A $100M E-commerce company runs all their data models using Scribble Enrich as the underlying engine to provide feature sets. The customer was able to quickly build and iterate on a number of models, to solve problems from Product Listing sort, to Re-ordering, and Assortment. The first versions of these models were enabled within two months of the engagement with 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.