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dotData Releases AI ML Updates in Data Science Automation

Significant Enhancements Include Feature Engineering for Text Data, Deep Learning Capability in AutoML, and More Flexible and Secure Hadoop Deployment

dotData, the first and only company focused on delivering full-cycle data science automation and operationalization for the enterprise announced the availability of Version 1.6 of dotData Data Enterprise and Version 1.2 of dotDataPy. The new updates add significant enhancements to both versions of its data science automation platform, to provide users with deeper insights, increased flexibility, and greater security to meet their specific business goals.

dotData provides solutions that dramatically improve the productivity of data science projects, which traditionally require extensive manual effort from valuable and skilled resources, by automating the data science process utilizing its proprietary Artificial Intelligence (AI) technologies. dotData was recently named a Leader in The Forrester New Wave™: Automation-Focused Machine Learning (AutoML) Solutions, Q2 2019.

“One of the most exciting enhancements in the update is the ability to automatically generate features from text data in combination with other types of data sources. This new feature unlocks the tremendous value of in-house business text data owned by many enterprises,” said Ryohei Fujimaki, Ph.D., founder, and CEO of dotData. “Another notable enhancement is the support of Deep Learning frameworks such as TensorFlow and PyTorch in our AutoML with enhanced transparency for highly non-linear ML models.”

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Key updates of the dotData Enterprise Version 1.6 and dotDataPy Version 1.2 include:

AI-powered Feature Engineering for Text Data

dotData now supports automated feature engineering for text data and unlocks valuable, but difficult to analyze text information such as call center customer feedback, sales reports, meeting minutes, etc. This feature is truly unique to dotData in that users can now analyze text data in combination with other types of information to automatically produce more profound, more valuable insights from data such as text transaction data, geo-locational text data, and temporal text data as well as traditional “static” text data, delivering new levels of value to enterprises.

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Deep Learning in AutoML

dotData now supports state-of-the-art Deep Learning frameworks such as TensorFlow and PyTorch as part of its AutoML capability. Now, data scientists, as well as “citizen” data scientists, can quickly test out advanced neural network methods. Additionally, to enhance model transparency for the advanced but “black-box” models, advanced feature validation methods such as permutation importance are now supported and available both on dotData GUI and dotDataPy.

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More Flexible and Secure Hadoop Deployment

dotData Enterprise Version 1.6 now supports more flexible deployment on an existing Hadoop cluster, enabling users to leverage their existing IT infrastructure to introduce dotData. Additionally, in addition to Kerberos (supported in dotData Enterprise 1.4), dotData 1.6 supports more advanced Hadoop security options to protect enterprise data.

More Enhancements for Greater Transparency

There are more enhancements available in the new versions. For example, “progress visualization” provides greater transparency of task status which is particularly useful in multiuser environments. Additionally, the Model Insights dashboard provides comparisons of many machine learning models in terms of various model metrics, enabling users to analyze model behavior deeply.

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