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dotData Launches dotData Py Lite, Putting the Power of AI Automation on Every Data Scientist’s Laptop

Install AI automation in one minute, develop features and ML models in ten minutes, deploy containerized AI instantly

dotData, a pioneer in AI automation and operationalization for the enterprise, announced the launch of dotData Py Lite, a new containerized AI automation solution to enable data scientists to execute quick POCs and deploy dotData on their desktop. Designed for Python data scientists, dotData Py Lite offers dotData’s award-winning automated feature engineering and automated machine learning (ML) in a portable environment, allowing data scientists to explore 100x more features, augment their hypotheses, and improve their ML models quickly without having to rely on large and expensive enterprise-AI environments.

Features and benefits of dotData Py Lite include:

  • All features and functionality of dotData’s award-winning automated feature engineering and AutoML
  • Containerized predictions from data through feature to ML scoring
  • One-minute installation on Windows, MacOS or Linux
  • Minimum resource requirements (2 CPU cores and 4GB of RAM)
  • Fully compatible with cluster-based dotData Py and dotData Enterprise deployment for scale-out

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“Great machine learning algorithms do not guarantee great AI models — the secret is feature engineering. Whether using machine learning for product demand forecasting, customer churn, revenue recovery, or failure detection, building strong features is difficult but critical to developing accurate predictions,” said Ryohei Fujimaki, Ph.D., founder and CEO of dotData. “dotData Py Lite was created to put the power of enterprise-grade automated feature engineering on everyone’s laptop. It takes one minute to install, ten minutes to develop, and deploys instantly.”

dotData Py Lite is designed to support the following three use cases:

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  • Quick and affordable environment for AI and ML experiments via AI automation for those who just started their AI/ML journey or who are exploring AI automation capabilities
  • Powerful yet easy library to explore a broad range of feature hypotheses via automated feature engineering for data scientists
  • Simple and portable way to deploy and productionalize E2E AI pipelines from data and feature engineering to ML scoring as AI micro-services via automated containerization for IT and engineering teams

dotData will showcase dotData Py Lite at the Gartner Data & Analytics Summit, taking place virtually. Dr. Fijumaki will also present a session at the Summit, discussing how automating the feature engineering process can dramatically improve the quality of AL and ML models and the speed of execution. His presentation, “Why Automated Feature Engineering Is The Key to Building Great AI Models,”.

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dotData automates feature engineering, the most manual and time-consuming step in AI and ML projects. dotData’s proprietary AI technology automatically discovers hidden patterns behind hundreds of tables with complex relationships and billions of rows and AI-features for your AI and ML algorithms. Until now, feature engineering has 100 percent relied on intuition and experience of domain experts and data scientists. With dotData, you can leverage AI to discover unknown-unknowns and build greater AI and ML models.

Experienced data science teams can leverage dotData’s AI features to augment in-house developed features. Automated feature engineering (AutoFE) provides a fast and automated means to rapidly prototype use cases, explore new datasets to find important patterns, and improve accuracy of AI and ML models. It is available as a Python library seamlessly integrated with your existing Python workflow, and cuts 80 percent of time to develop features for your AI and ML models.

Business intelligence and analytics teams can leverage dotData’s no-code AI/ML automation solution to make their reporting and dashboards more predictive and actionable. It offers a streamlined integration of AutoFE and automated machine learning (AutoML) and allows you to develop production-ready features and ML models from raw business data, in just days.

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