Monitaur Use Cases

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.

  • transparency


    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 Record establishes transparency by ensuring every model and decisions are recorded, versioned, understandable, and accessible. Transparency is fundamental to creating ML Assurance.

  • compliance


    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 Monitaur Record, Monitaur Audit allows non-technical users to access ML transactions at any time, enabling objective evaluation, review, and testing of every transaction across ML applications captured by Monitaur Record. Our Monitor product provides proactive alerts when important control thresholds are crossed.

  • fairness


    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. Monitor has specific bias monitoring and alerts, but also allows users to configure key bias-related controls. Monitaur's organization of decisions with Record and enablement of inspections with Audit combine with Monitor to enable a full workflow of assurance management for ML systems.

  • safety


    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 Monitor 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 Assure.

  • machine learning operationalization

    ML Systems Operationalization

    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 scalable infrastructure around your ML system is a new challenge, with a unique complexity that requires specialized skills across the disciplines of data science, engineering, and business. Monitaur Record is a golden corpus for all of your ML transactions, decisions, model versions, and configurations, 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.