According to a new press release, “dotData, the first and only company focused on delivering end-to-end data science automation and operationalization for the enterprise, today announced the availability of Version 1.2 of its dotData Platform. The new version adds significant enhancements to the platform, enabling users to have even deeper insights, more transparency, and greater business impacts in the development and operationalization of their data science projects. The AI-powered dotData Platform completely automates the entire data science process, from data collection through production-ready models. As a result, the entire data science process is accelerated from months to days, enabling companies to rapidly scale their AI/ML initiatives to drive transformative business changes.”
The release continues, “The dotData Platform also democratizes the data science process by enabling more participants with different skill levels to effectively execute on projects, making it possible for enterprises to operationalize 10x more projects with transparent and actionable outcomes. ‘The new enhancements available in Version 1.2 are significant in that they add measurable benefits to users,’ said Ryohei Fujimaki, PhD, dotData’s CEO. ‘We can now provide even stronger features, easier model operationalization, greater transparency, and deeper insights’.”
The release adds, “Key updates of the dotData Platform include: (1) New Attribute Features Are now automatically designed by dotData’s AI-powered feature engineering. Attribute features are critical in use cases where very limited historical data is given, e.g., making product recommendations to a new customer about whom little is known. Attribute features are generated by taking into consideration customers with similar attributes, offering powerful predictions in these types of challenging use cases. (2) Enhanced Model Operationalization Enables IT/Software engineers to redesign features and retrain machine learning models via dotData Retraining APIs. This enhancement eliminates the periodic and manual maintenance of the operationalized features and machine learning models in production.”
Read more at PR Newswire.
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