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RESEARCH & RESOURCES

DotData Launches Containerized AI Model for Real-Time Prediction

Highly scalable AI/ML container can be deployed in the cloud for ML orchestration or at the edge for intelligent IoT.

Note: TDWI’s editors carefully choose press releases related to the data and analytics industry. We have edited and/or condensed this release to highlight key features but make no claims as to the accuracy of the author's statements.

DotData, a leader in full-cycle data science automation and operationalization for the enterprise, has launched dotData Stream, a new containerized AI/ML model that enables real-time predictive capabilities for dotData users. DotData Stream was developed to meet the growing market demand for real-time prediction capabilities for use cases such as fraud detection, automated underwriting, dynamic pricing, and industrial IoT.

DotData Stream performs real-time predictions using AI/ML models developed on the dotData Platform, including feature transformation such as one-hot encoding, missing-value imputation, data normalization, and outlier filter. It is highly scalable and fast; a single prediction can be performed as fast as tens of milliseconds or even faster for microbatch predictions.

Deploying dotData Stream is as easy and simple as launching a docker container with AI/ML models downloaded from the dotData Platform with just one click. An endpoint for real-time predictions becomes immediately available. In addition, dotData Stream can run in cloud MLOps platforms for enterprise AI/ML orchestration or at the edge servers for intelligent IoT applications.

DotData provides AutoML 2.0 solutions that help accelerate development of AI and machine learning (AI/ML) models for advanced predictive analytics BI dashboards and applications. DotData makes it easy for BI developers and data engineers to develop AI/ML models in just days by automating the full life cycle of the data science process, from business raw data through feature engineering to implementation of ML in production utilizing its proprietary AI technologies.

DotData's AI-powered feature engineering automatically applies data transformation, cleansing, normalization, aggregation, and combination, and transforms hundreds of tables with complex relationships and billions of rows into a single feature table, automating the most manual data science projects that are fundamental to developing predictive analytics solutions.

DotData democratizes data science by enabling BI developers and data engineers to make enterprise data science scalable and sustainable. DotData automates up to 100 percent of the AI/ML development workflow, enabling users to connect directly to their enterprise data sources to discover and evaluate millions of features from complex table structures and huge data sets with minimal user input.

It is also designed to operationalize AI/ML models by producing both feature and ML scoring pipelines in production, which IT teams can then immediately integrate with business workflows. This can further automate the time-consuming and arduous process of maintaining the deployed pipeline to ensure repeatability as data changes over time. With the dotData GUI, AI/ML development becomes a five-minute operation, requiring neither significant data science experience nor SQL/Python/R coding.

For more information or a demo of dotData's AI-powered full-cycle data science automation platform, visit dotData.com.

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