Algorithmia tackles ML compliance with new governance tools

Algorithmia, an MLOps software company, has released a set of tools to help technology leaders manage compliance in machine learning models.

The Seattle-based company worked off the back of research suggesting 56 per cent of IT leaders thought ML governance was a major concern. Ramifications could include bad credit decisions, fraud detection errors or poor decision making that was visible to clients.

Significant analytics risk

Algorithmia, which specialises in MLOps, has developed a set of tools to help technology leaders with post-deployment risks in machine learning models. Operational risk is now the most significant analytics risk, according to the company.

Algorithmia Enterprise’s new product offers five key 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.

Diego Oppenheimer, CEO of Algorithmia, said, “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.

“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.”