DataPrepOps is the operationalization of Data Preparation.
It’s full-stack Data Engineering for Machine Learning Data.
In short, it’s the process of applied technology and engineering best practices to convert raw data into ML-ready data.
Interested in the story and the technical details around DataPrepOps? Then read on, you’re in the right place!
The Genesis of MLOps
Machine Learning is complicated. And if you think that developing a model is hard, just try putting that model to production. That’s exactly why MLOps was invented: to enable any organization to deploy Machine Learning models to production seamlessly without the need to hire a full team of DevOps engineers and ML engineers.
But what is MLOps? MLOps is essentially DevOps for Machine Learning models, and it revolutionized Machine Learning in the late 2010s by enabling hundreds of organizations to push their models to production.
Preventing the next AI Winter
Traditional MLOps platforms might help put a model in production, but it won’t help reduce the costs of keeping that model running. That’s where DataPrepOps can help.
The advent of MLOps prevented countless ML projects from failing by ensuring that the models built by data scientists could be monetized as data products. But even with their models in production, organizations faced a major issue because of the astronomical cost of training and retraining to keep those models up to date. So those same projects were at risk again, this time not because companies could not monetize on it, but because of the absence of ROI.
That’s what we call the AI Cost Chasm, and our platform is designed to help you cross it!