New releases empower data science teams to build, deploy, and manage more reliable predictive models faster with interactive result sharing, powerful automated data quality checks, shadow testing, and automated model retraining.
SAN MATEO, California, August 20, 2025 — dotData, a pioneer and leading provider of AI-powered insight discovery platforms, today announced product updates for dotData Feature Factory 1.4 and dotData Ops 1.5. The releases introduce powerful new capabilities. The updates are designed to enhance collaboration, improve model interpretability, and automate critical MLOps processes, empowering organizations to build and deploy more reliable AI faster.
Key Features of dotData Feature Factory 1.4 Include:
Support Microsoft Fabric Deployment
dotData Feature Factory 1.4 can now be deployed directly within Microsoft Fabric, enabling Microsoft Azure users to build powerful features without needing to move or duplicate data. This integration unifies data governance under the Fabric umbrella, offering unprecedented scalability, data security, and flexibility for the most demanding AI workloads.
Advanced Data Integrity Checks to Proactively Resolve Issues
The new data checks automatically detect a wide range of data quality issues, including constant columns, categorical codes in numeric fields, and unparsed values. Users can now review these findings and apply suggested code fixes directly from the UI, transforming data cleaning from a manual chore into a quick, interactive process that boosts the reliability of every analysis.
Seamless Collaboration with Portable Interactive Output
To accelerate the feedback loop between technical and business teams, users can now export feature leaderboards and other widgets as standalone HTML files. This enables any stakeholder, regardless of technical expertise or platform access, to explore and understand discovered features, fostering alignment and accelerating the validation of features for production use.
Key Features of dotData Ops 1.5 Include:
Automated Model Retraining and Management
The release introduces a Champion/Challenger framework, allowing a deployed “Champion” model to be tested against a new “Challenger” model. A challenger can be introduced by either uploading an existing model file or by using a new, automated retraining task that leverages state-of-the-art AutoML to find the optimal model based on fresh data.
Risk-Free Shadow Testing and Intelligent Model-Promotion
Run Challenger models alongside Champion models on live data to compare performance and feature importance without risk. If a Challenger meets user-defined criteria—such as outperforming in three of the last four runs—it can be automatically promoted, ensuring production always uses the best model.
In addition to the above key updates, dotData Ops 1.5 and dotData Feature Factory 1.4 include various improvements in robustness, ease-of-use, and automation for managing the entire lifecycle of machine learning models, from discovering new features in previously untapped data to deployment and monitoring, as well as retraining and optimization.
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dotData's pioneering Statistical AI technology addresses the most complex challenge of modern analytics and AI projects: uncovering the nuanced, hidden gems of insight that were previously trapped in unexplored data. Our award-winning engine navigates the most complex and convoluted data structures by connecting the dots within large-scale datasets in hours, rather than days, discovering high-value, transparent, and explainable signals. dotData enables organizations to eliminate human bias and reduce costs by exploring 100 times more signals, including those yet to be imagined. The power of dotData’s technology is providing game-changing insights for Fortune 500 organizations around the world.
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