ACCELERATED MACHINE LEARNING
From Lab to Real World
The DIligence To Create Fit-For-Purpose ML
There’s an explosion of ML model deployments across industries and throughout organisations. Enterprise data science teams are turning to ML in a big way. At any point, they’re piloting multiple ML models to unlock opportunities, or increase organizational efficiency. These models are compute-intensive, and only as good as the data they’re working with. They need to be fed by robust ML engineering machinery.
At Scribble, we’ve learnt what solid ML engineering is - how laborious, expensive and front-loaded it can be - and what it takes to design, develop and operate the software infrastructure to accelerate your ML journey robustly.
We are an ML engineering company, helping Enterprises productionize their ML Models. There’s no magic to it, nor any ‘click-to-deploy’ solutions.
Our flagship product, Enrich, is a feature engineering platform. It enables ML engineers and data scientists to build sound features, train models, and set up robust data pipelines to ensure that these models run to spec in production.
Let's do the math.