Algorithmia Touts New Governance Capabilities
March 5, 2021
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
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
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
and usage reporting on infrastructure, storage and compute consumption within
Algorithmia to understand and manage the overall cost of maintaining the
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.
“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.”