DataRobot, the leader in enterprise AI, announced enhancements to its enterprise AI platform designed to take enterprise AI to new heights. These enhancements include a Use Case Value Tracker, Location AI, Champion/Challenger Models and Humble AI for MLOps, and Anomaly Detection for Time Series. Details were announced this morning at DataRobot’s AI Experience Worldwide, a virtual, two-day event aimed at helping organizations effectively apply AI to enhance agility, improve customer service and retention, foster innovation, and bolster overall business performance.
“We believe that understanding the business value from your AI investments is a critical gap in the industry — and one that we are closing with this release”
Today, most enterprises are using or evaluating AI in some capacity. According to a December 2019 survey conducted by O’Reilly, 85% of organizations are currently evaluating AI or using it in production. And AI investments are only growing: a recent IDC report found that spending on AI systems will increase by 31% in 2020 from 2019. As organizations continue to adopt AI and scale the technology enterprise-wide, it’s critical they have the support needed to experience optimal value. DataRobot’s new enhancements will empower customers to derive and extract even more value from their AI investments.
In the latest version of the platform, DataRobot has introduced:
- Use Case Value Tracker: A central hub to collaborate with team members on end-to-end AI initiatives. It allows users to manage and organize machine learning project assets around use cases and understand the ROI of the predictions they make.
- Location AI: Location AI is a patent pending feature that allows users to add geospatial data to their predictive models. This helps predictive models understand the spatial relationships between observations. Knowing the effects of proximity is critical for many prediction problems, and Location AI automates complex and specialized spatial modeling tasks for both novice and expert users.
- Humble AI: With Humble AI, users can set specific conditions to trigger in real-time when a model does not have confidence in a prediction. Once a condition for a trigger is met, users can choose to either proceed with the prediction, force the model to make a safe prediction of their choice, or return an error instead. This builds on DataRobot’s existing model monitoring capabilities by allowing customers to put custom guardrails in place, lowering risk and increasing trust for every single prediction.
- Champion/Challenger Models for MLOps: Champion/Challenger Models unlock the ability in DataRobot MLOps to test and compare production models with alternative models to see which perform the best over time. If a challenger model beats a user’s current champion, users can hot-swap models with no service interruption.
- Automated Time Series Anomaly Detection: Time Series Anomaly Detection is a fully unsupervised machine learning workflow that allows users to detect anomalies without specifying a target variable. DataRobot automatically selects, builds, tests, and ranks a diverse set of anomaly detection models, unlocking a wide variety of new use cases for Automated Time Series customers.
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