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Introducing dotData Py Lite

March 15, 2021

Evaluating a data science platform is a daunting task – comparing features, exploring benefits, building consensus between stakeholders takes a lot of time. Data scientists prefer a code-first approach and use multiple tools to build ML models. On the other hand, citizen data scientists and newly minted data scientists take a liking to no-code methods and use AutoML tools. There is no single platform that meets the needs of experienced data scientists and citizen data scientists. With the increasing adoption of AI and ML in business applications, such as adding prediction capabilities to BI, there is a need for tools where the experienced data scientists can help others, say BI analysts, and port the work across platforms. dotData Py Lite was built to address these problems.

What is dotData Py Lite?

dotData Py Lite is our latest product that allows users to install a dotData environment on their local machine. It is a containerized AI automation engine that enables data scientists to perform small experiments, execute quick POCs, and evaluate the dotData platform right from their desktop. It offers dotData’s award-winning automated feature engineering and automated machine learning (ML) in a portable environment meant for desktop deployments. dotData Py on your desktop allows data scientists to explore 100x more features, augment hypotheses, and improve ML models quickly without having to rely on large and expensive enterprise-AI environments.

dotData Py Lite works with multiple tools to forecast and analyze data.

Who is it for? What problems does it solve?

dotData Py Lite is designed for Python data scientists. With Py Lite, data scientists can quickly start their evaluation of dotData more easily and leverage the right dotData products such as dotData Enterprise, dotData Py, and dotData Stream based on the application requirement. Users can use Py Lite as a lightweight model deployment way similar to dD Stream. For example, in order to integrate our feature engineering engine as a part of the Tableau pipeline, you will need a Python interface with a single node web-backend and dotData Py is that interface.

What benefits does it have?

  • 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

What are some of the Use Cases?

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 end-to-end AI pipelines from data and feature engineering to ML scoring as AI micro-services via automated containerization for IT and engineering teams
  • Curious to learn more, check out details about dotData Py Lite here:

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dotData Inc.

dotData Automated Feature Engineering powers our full-cycle data science automation platform to help enterprise organizations accelerate ML and AI projects and deliver more business value by automating the hardest part of the data science and AI process – feature engineering and operationalization. Learn more at, and join us on Twitter and LinkedIn.