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 brand new containerized AI automation resolution to allow information scientists to execute fast POCs and deploy dotData on their desktop.

Designed for Python information scientists, dotData Py Lite affords dotData’s automated characteristic engineering and automatic machine studying (ML) in a transportable surroundings, permitting information scientists to discover 100x extra options, increase their hypotheses, and enhance their ML fashions shortly with out having to depend on giant and costly enterprise-AI environments, in response to the seller. 

Features and advantages of dotData Py Lite embody:

  • All options and performance of dotData’s automated characteristic engineering and AutoML
  • Containerized predictions from information by characteristic to ML scoring
  • One-minute set up on Windows, MacOS or Linux
  • Minimum useful resource necessities (2 CPU cores and 4GB of RAM)
  • Fully suitable 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,” stated 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 assist the next three use circumstances:

  • Quick and inexpensive surroundings for AI and ML experiments by way of AI automation for individuals who simply began their AI/ML journey or who’re exploring AI automation capabilities
  • Powerful but simple library to discover a broad vary of characteristic hypotheses by way of automated characteristic engineering for information scientists
  • Simple and transportable method to deploy and productionalize E2E AI pipelines from information and have engineering to ML scoring as AI micro-services by way of automated containerization for IT and engineering groups

For extra details about this providing, go to


Please enter your comment!
Please enter your name here