Arize AI Introduces Next Generation Of Its Machine Learning Observability Platform, Goes Self-Serve For Any Organization Seeking To Optimize AI Investments

Arize AI, the leader in machine learning (ML) observability and model performance monitoring, introduced the next generation of its ML observability platform at its Arize:Observe 2022 summit.

Arize is the industry’s first and only full-stack ML observability and model performance monitoring platform that is built specifically to solve troubleshooting bottlenecks and pain points experienced every day by thousands of ML engineers, data scientists and other practitioners responsible for deploying and maintaining ML models.

With this release, Arize marks a milestone in its evolution, becoming the first ML observability company to offer a full complement of self-serve signup options for every organization – including a free offering that makes it easy for ML engineers to get up and running in minutes.

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The next-generation Arize platform is battle-proven, deployed by some of the world’s most respected and advanced ML organizations to help quickly detect issues the moment they emerge, troubleshoot why they happened, and improve overall model performance. In all, Arize processes hundreds of billions of predictions a month.

Included in the release are enhancements to platform features used every day by ML engineers tasked with solving some of their organizations’ most important challenges, allowing teams to better:

Monitor and Identify Drift–Pinpoint drift across model dimensions and values. Track for prediction, data, and concept drift across model dimensions and values, and compare across training, validation, and production environments.

Ensure Data Integrity–Guarantee the quality of model data inputs and outputs with automated checks for missing, unexpected, or extreme values.

Improve Model Performance–Use ML performance tracing to automatically pinpoint the source of model performance problems and map back to underlying data issues.

Leverage Explainability–See how a model dimension affects prediction distributions, and leverage SHAP to explain feature importance for specific cohorts.

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Introducing Arize’s New Self-Serve Options

An early pioneer and leader in machine learning (ML) observability and monitoring, Arize AI already tracks hundreds of billions of predictions a month on behalf of large enterprises and disruptive startups.

Arize’s newly released self-serve options remove barriers to adoption to ensure that every organization can detect, root cause, and resolve model performance issues faster regardless of the number of models deployed in production.

New users can sign up here. Featuring an easy integration via an SDK or file ingestion from major cloud storage providers, Arize enables ML teams to begin monitoring and troubleshooting model performance in minutes.

“The reality today is that most teams are only doing ‘red light; green light’ model monitoring and haven’t yet embraced true ML observability with ML performance tracing to pinpoint the source of model performance problems before they impact customers or the bottom line,” said Arize Co-Founder and Chief Product Officer Aparna Dhinakaran. “We are changing that with a platform that is purpose-built to tackle the toughest ML observability challenges of the world’s most respected organizations. Customers of all sizes are now able to try, buy and deploy our AI model monitoring capabilities and expand their model coverage as their needs change.”

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Free Offering Jumpstarts AI Observability and Model Monitoring

In a recent survey of more than 900 data scientists, engineers and executives, Arize found that 84.3% of data scientists and ML engineers say the time it takes to detect and diagnose problems with a model is an issue for their teams at least some of the time. This challenge is most significant when teams are reliant upon solutions that are not optimized to detect, root cause, and quickly resolve model performance issues.

New Arize customers can now select from Free, Pro, Business and Enterprise versions that map directly to the number of models, features used and predictions in production. Any organization that deploys any Arize tier can easily add new capacity and advanced capabilities as their needs expand.

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