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Newly Released dotData Py Lite Enables Python AI Development on Desktop

The dotData product is designed for data scientists developing ML models and deploying containerized AI.

Note: TDWI’s editors carefully choose vendor-issued press releases about new or upgraded products and services. We have edited and/or condensed this release to highlight key features but make no claims as to the accuracy of the vendor's statements.

AI automation vendor dotData released dotData Py Lite, a containerized AI automation solution designed to enhance execution of proof of concepts (POCs) and deployment of dotData applications. Designed for Python data scientists, dotData Py Lite offers dotData’s automated feature engineering and automated machine learning (ML) in a portable environment, allowing data scientists to augment their hypotheses, and refine their ML models without having to rely on large enterprise-AI environments.  

Features and benefits of dotData Py Lite include: 

  • All features and functionality of dotData’s automated feature engineering and AutoML
  • Containerized predictions from data through feature to ML scoring
  • Installation on Windows, MacOS, or Linux
  • Fully compatible with cluster-based dotData Py and dotData Enterprise deployment for scale-out

Designed to support the following three use cases:

  • Desktop and laptop environments for AI and ML experiments via AI automation for those who just started their AI/ML journey or who are exploring AI automation capabilities
  • A library to explore a broad range of feature hypotheses via automated feature engineering for data scientists
  • A portable way to deploy exchange-to-exchange (E2E) AI pipelines from data and feature engineering to ML scoring as AI microservices via automated containerization for IT and engineering teams

The dotData product is designed to automate feature engineering, the most manual and time-consuming step in AI and ML projects. The company’s AI technology is designed to automatically discover hidden patterns behind hundreds of tables with complex relationships and billions of rows and AI-features for AI and ML algorithms.

Experienced data science teams can use dotData’s AI features to augment in-house developed features. Automated feature engineering (AutoFE) enables prototyping of use cases and exploration of new data sets to find patterns to help improve the accuracy of AI and ML models. It is available as a Python library integrated with users’ existing Python workflow.   

Business intelligence and analytics teams can use dotData’s no-code AI/ML automation solution to make their reporting and dashboards more predictive and actionable. It offers an integration of AutoFE and automated machine learning (AutoML) for  developing production-ready features and ML models from raw business data. 

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