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July 14, 2020
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.
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 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.
dotData Py Lite is designed to support the following three use cases:
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