Reliable Model Deployment
There is significant complexity in the deployment of analytics models. Providing predictions to other planning systems requires a different skillset in the data science toolbox than model design. Deploying predictions differs substantially from traditional software development. If you do not understand the mechanics behind the machine learning model, you run the risk of displaying inaccurate and untrustworthy insights.
Some of the world’s most innovative companies struggle with analytics deployment, referenced by Google’s paper on Hidden Technical Debt in ML.
Mosaic relies on a set of tools and heuristics developed from years of getting model insights into decision-makers hands. Predictions are only as good as the data going in, data pipelining, feature selection, and evaluation metrics are all vital to facilitate production-grade applications.
Mosaic does not think about the reliable model deployment in isolation. Our deployments are planned at a system level. Relying on techniques like MLOps, we provide ongoing system maintenance, algorithm updates, auditing services, and model drift monitoring.
The technical debt starts to add up quickly.
Customers Who Have Used This Engagement Model
How to Get Started
Stuck deploying & maintaining a custom application – Mosaic is here to help. Please let us know what technical challenges you are facing, and we will let you know if & how we can assist.