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Algorithmia Tackles Machine Learning Model Risk With New Governance Capabilities

  • The MLOps software leader is responding to the need large organizations have to manage ML models in production within their existing governance, risk and compliance frameworks

Algorithmia, a leader in machine learning (ML) operations and management software, has released new, advanced reporting tools to help enterprise IT and internal risk leaders govern the use of ML models in production environments.

According to Algorithmia’s 2021 Enterprise Trends in Machine Learning report, the top ML challenge facing organizations today is governance. 56% of IT leaders responding to Algorithmia’s survey ranked governance, security and auditability issues as a major concern; 67% of all respondents reported needing to comply with multiple regulations. The effects of a model failure may not be known for some time, perhaps after bad credit decisions, fraud detection decisions or client-visible decisions have been made. Current model governance approaches are not sufficient or not being applied appropriately to machine learning operations (MLOps).

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In most organizations, governance and ML model risk management are primarily focused on validation and testing of models and inspection of documentation prior to model deployment. As ML adoption has accelerated over the last year, IT leaders, business line leaders, CIOs and chief risk officers have realized that what happens after a model is deployed is even more important than pre-deployment testing and validation. Operational risk is now the most significant analytics risk.

Advanced Reporting & Governance Capabilities

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The launch today of Algorithmia’s advanced reporting capabilities for governance fills out the compliance and audit capabilities of its Enterprise product. It also augments existing Algorithmia capabilities around explainability and performance monitoring (available in Algorithmia Insights), model cataloging, repository and security.

Algorithmia’s Enterprise product now provides the following reporting and governance capabilities:

  • Cost and usage reporting on infrastructure, storage and compute consumption within Algorithmia to understand and manage the overall cost of maintaining the platform.
  • Enhanced chargeback and showback reporting for monthly costs of storage, CPU and GPU consumption and usage billing.
  • Algorithm usage reporting with details of the algorithm used, so organizations can bill users for their usage.
  • Enhanced audit reports and logs so examiners and auditors can review model results, history of changes, and a record of data errors or past model failures and actions taken.
  • Advanced reporting panel for Algorithmia admins that provide an overview of all available metrics and usage reporting, ability to build reports and export reports and metrics to systems of record.

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“We’re still in the early days of ML governance, and organizations lack a clear roadmap or prescriptive advice for implementing it effectively in their own unique environments,” said Diego Oppenheimer, CEO of Algorithmia. “Regulations are undefined and a changing and ambiguous regulatory landscape leads to uncertainty and the need for companies to invest significant resources to maintain compliance. Those that can’t keep up risk losing their competitive edge. Furthermore, existing solutions are manual and incomplete. Even organizations that are implementing governance today are doing so with a patchwork of disparate tools and manual processes. Not only do such solutions require constant maintenance, but they also risk critical gaps in coverage.”

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