Principles of ML Assurance

Your business is eager to invest in machine learning, but the "black box" of ML decisions creates too many questions, too many unknowns, too many risks.

Failure to effectively identify and manage these needs can lead to catastrophe at the worst and unrealized opportunity at the least. Tackling them with Monitaur unlocks the massive opportunities and innovation that machine learning holds for your business.


For machine learning applications, transparency gives you a common, accessible understanding of everything that your systems have done. You can access any event, any decision, and any outcome at any time, simplifying ongoing compliance and audit needs.

Depending on your industry, your customers or regulators may need to understand how your system made decisions. Transparency enables you to respond immediately and easily.

Monitaur RecordML establishes transparency by ensuring every model and decisions are recorded, versioned, understandable, and accessible. Transparency is fundamental to creating ML Assurance.
Transparent machine learning and AI


Regulated industries face emerging standards and requirements for initiatives that rely on machine learning. By shining a light inside the ML "black box", members of your compliance team can prove that your ML meets the internal and external expectations.

Managing compliance requires both reactive and proactive efforts. After securing transparency with RecordML, AuditML allows non-technical users to access ML transactions at any time, enabling objective evaluation, review, and testing of every transaction across ML applications captured. Our MonitorML product provides proactive alerts when important control thresholds are crossed.
Compliant machine learning and AI


As ML algorithms alter themselves, the specter of bias naturally enters the equation. From the perspectives of both regulatory risk and reputational harm, fairness is one the more important responsibilities of doing business today. You need to ensure that your artificial intelligence is not discriminating against protected classes and offering equitable access to your products.

Managing fairness often falls within the domain of the compliance team, but the task becomes much harder with ML and requires new visibility and connectivity between various teams and systems. MonitorML has specific bias monitoring and alerts, but also allows users to configure key bias-related controls. Monitaur's organization of decisions with RecordML and enablement of inspections with AuditML combine with MonitorML and GovernML to enable a full workflow of assurance management for ML systems.
Fair machine learning and AI


ML applications have made incredible in-roads in industries like healthcare and life sciences in recent years. In these cases, machine learning is often making core decisions with enormous personal impact. With life and limb at stake, your company needs to understand and manage the risks proactively and continuously.

Our MonitorML product drives assurance for the safety of ML by watching for drift and anomalies across all of your ML deployments. You can also take advantage of our deep expertise to improve and validate your overall program through Monitaur GovernML.
Safe machine learning and AI


Recent decades of investment in ML have been focused on research and development. Now companies face the challenges of deploying ML in reliable, repeatable, and responsible ways.

Establishing a robust, scalable infrastructure around your ML systems is a new challenge, with a unique complexity that requires specialized skills across the disciplines of data science, engineering, and business. Monitaur RecordML is a golden corpus for all of your ML transactions, decisions, versions, and configurations across all of your models, creating a centralized, single source of truth for all stakeholders.

By providing access to the owners of risk, compliance, and governance, software engineering and data science teams can focus all their attention on innovation and delivery, instead of digging through endless logs and trying to reconstruct how the ML arrived at specific decisions.
Optimized ML operations