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dotData Launches dotData Stream – Containerized AI Model for Real-Time Prediction

Highly Scalable and Effective AI/ML Container, Easily Deployable Either in the Cloud for ML Orchestration or at the Edge for Intelligent IoT

dotData, a leader in full-cycle data science automation and operationalization for the enterprise, 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, industrial IoT, and more.

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 effective – a single prediction can be performed as fast as tens of milliseconds or even faster for microbatch predictions. Its deployment is as easy and simple as launching a docker container with AI/ML models downloaded from the dotData Platform with just one click. An end-point 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.

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JFE Steel, one of the world’s leading integrated steel producers, recently implemented dotData to support the deployment of intelligent IoT in their manufacturing plants.

“After testing several leading autoML platforms, we chose dotData as we were impressed with dotData’s autoML 2.0 full-cycle automation of ML processes, including automated feature engineering on our manufacturing data,” said Mr. Kazuro Tsuda, Staff General Manager, Data Science Project Dept. JFE Steel Corporation. “JFE Steel has a vision to deploy various AI models to implement Cyber-Physical Systems in our steel manufacturing plants. dotData Stream will be a key component to realize our vision and JFE Steel is looking forward to expanding its partnership with the dotData team.”

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“We are seeing an increasing demand for real-time prediction capability, which has become an essential necessity for many enterprise companies. dotData Stream allows our customers to leverage AI/ML capability in a real-time environment,” said Ryohei Fujimaki, Ph.D., founder and CEO of dotData. “We are honored and excited about our partnership with JFE Steel. Their intelligent IoT application is the perfect use case to demonstrate the ability of dotData Stream, and we are fully committed to supporting their vision to adopt AI/ML in smart manufacturing and achieve the full potential of Industry 4.0.”

dotData provides AutoML 2.0 solutions that help accelerate the process of developing AI and Machine Learning (AI/ML) models for use in 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.  dotData 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.

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