Algorithmia is today debuting new reporting tools which aim to solve machine learning (ML) governance challenges.

Research conducted by the company found that the number one challenge organisations are facing with their deployments is governance.

56 percent of the IT leaders surveyed by Algorithmia ranked governance, security, and auditability issues as a major concern. 67 percent report having to comply with multiple regulations—of which the penalties of failing to do so can be severe.

Diego Oppenheimer, CEO of Algorithmia, said:

“We’re still in the early days of ML governance, and organisations 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 organisations 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.”

Organisations test ML models, quite rightly, prior to deployment. However, Algorithmia says that IT leaders, business line leaders, CIOs and chief risk officers have realised over the past year – following an acceleration in ML deployments – that what happens after a model is deployed “is even more important than pre-deployment testing.”

As of today, Algorithmia’s Enterprise product has added the following reporting and governance capabilities to help manage operational risk:

• 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.

The new reporting and governance tools added to Algorithmia will help IT decision-makers tackle one of the biggest challenges they face with deployments today.

(Photo by Belhadj lamine on Unsplash)

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Tags: algorithmia, Featured, governance, machine learning, ml model, reporting, tools