dotData Offers AI Automation Solution for Python Data Scientists

dotData, a pioneer in AI automation and operationalization for the enterprise, is releasing 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 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, according to the vendor. 

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
  • 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

“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:

  • 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

For more information about this offering, visit