Monitaur is a comprehensive model management and assurance platform that enables recording, understanding, verification, and auditing of your machine learning models. The platform is environment agnostic, with support for all classical machine and deep learning implementations in Python, R, and Java.
For companies in regulated industries using models to make decisions, Monitaur delivers transparency and auditability that's necessary to manage compliance and unlock innovation.
Monitaur helps forward-thinking companies deploying machine learning by provide the recording, understanding, verifying, and auditing you need in able to prepare for regulatory scrutiny or just provide peace of mind that your deployed models are doing what they are supposed to do. Having the assurance that your models are doing what they are designed to do allows a company to unlock innovation and increase top-line growth by deploying models that have previously stayed sandboxed and/or decrease cost compliance cost.
The Monitaur client library is designed to record your machine learning model's meta information along with hashed versions of the serialized model and production files. Each transaction is then recorded as it runs on your production infrastructure.
In addition, Monitaur versions your models based on changes to the production and trained model file hashes. When changes occur, the platform automatically generates alerts. You can also define your own alerting logic based on transaction-level details.
As transactions are sent to the back-end API, Monitaur obtains the underlying influences for each of your model's decisions via open source interpretability libraries. This, along with the ability to create configurable alerts, Monitaur provides insight into all of your model needs.
The Monitaur GRC (Governance, Risk, and Compliance) web application is designed specifically for the nontechnical user to find transactions.
With full model versioning and transaction reproducibility, the platform allows counterfactuals for auditibility of machine learning models. With metrics for model and feature drift, along with proactive bias controls, Monitaur can provide automated model audit reports. Inside Monitaur, we provide model white paper creation and workflow processes around proven machine learning governance frameworks.
Machine Learning models are being used to reduce readmissions and optimize healthcare quality. With Monitaur, an audit trail is created that is verifiable, with full model versioning and transaction reproducibility to provide assurance around model decisions.
A Machine Learning classification model, such as Gradient Boosted Machine, can be used to determine if a customer will default on their loan payments. Monitaur provides recording, understanding, verifiability, and auditiblity to provide peace of mind and assurance for companies in regulated industries using models to make decisions.