Truera Removes the “Black Box” Surrounding Machine Learning by Emerging from Stealth with its Platform

Truera, provider of a model intelligence platform, is emerging from stealth to launch its technology solution that removes the “black box” surrounding machine learning (ML) and provides intelligence and actionable insights throughout the ML model lifecycle.

The company also announced it has raised a $5.1-million first round of funding, led by Greylock with additional investors including Wing VC, Conversion Capital, and Aaref Hilaly.

This black box problem makes it challenging for data scientists to build high quality models that not only achieve test accuracy hurdles but also generalize and perform well in the real world.

Business partners, regulators, operators, and customers find it harder to trust and adopt ML-powered applications.

Black box models raise societal concerns about fairness, bias, and transparency. It’s harder to maintain black box ML model performance and trust over time when new data changes from the training data used to create the model - a challenge called “concept drift” that is top of mind during the current Coronavirus pandemic.

Truera’s Model Intelligence platform has been designed from the ground up to solve these black box problems.

Truera’s AI.Q technology—the basis for its platform—is an enterprise-class AI explainability technology. It performs sophisticated sensitivity analysis that enables data scientists and non-data scientists to understand exactly why a model makes a prediction.

Current alternatives are less accurate, significantly slower and do not meet all of the enterprise and regulatory explainability requirements.

The Truera platform can be deployed on-premise or in a company’s private cloud in hours, to help customers:

  • Analyze and explain machine learning. Truera’s enterprise-class AI explainability enables data scientists to explain model predictions, and gain new insights into model behavior that can improve the development and operationalization of models.
  • Improve model quality. Achieving business results with ML requires building high-quality models that are accurate, stable, reliable, explainable and fair. Truera helps data scientists analyze and improve model quality so that models deliver better business results.
  • Build trust, reduce risk. Trusting black box ML models is hard. ML projects are also risky, subject to high rates of project failures, delays, over-budget spending, and compliance risks. Truera helps data science teams increase trust and address ML project risks.
  • Operationalize and monitor with confidence. AI applications need new monitoring and management oversight as operational data can “drift” from training data. Truera enables data scientists to address these unique monitoring and management challenges.

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