dotData Announces Automated Feature Engineering on the Databricks Platform
Empowers Databricks users to explore 10x more data and discover 100x more features to deliver greater ML models
SAN MATEO, Calif., July 20, 2021 – dotData, a leader in full-cycle enterprise AI automation solutions, today announced that its award-winning Automated Feature Engineering (AutoFE) technology is now fully integrated with and available on the Databricks Platform. This integration of dotData’s AutoFE with the Databricks Platform allows Databricks users to explore 100x more features and boost model accuracy quickly, augmenting domain features with hundreds of AI features.
dotData’s AutoFE is fully integrated with the Python data science workflow and explores millions of features from relational, transactional, temporal, geo-locational, and text data. It deals with multi-relational tables with billions of records and builds a ML-ready feature table just in hours.
Benefits of dotData’s AutoFE integration with the Databricks platform include:
- All features and functionality of dotData’s award-winning automated feature engineering and AutoML
- Leverages Databricks runtime to maximize the speed of feature engineering
- Compatibility with Databricks tool ecosystem, e.g. manage dotData’s AI-features by Databricks’ Feature Store
- Installed as a library, requiring no changes on the existing Databricks Python workbench
“Developing better ML models requires great features. The combination of dotData’s AutoFE with the Databricks platform empowers data scientists to deliver higher quality models faster,” said Ryohei Fujimaki, Ph.D., founder and CEO of dotData. “We have been seeing increasing interest and demand for our AutoFE solutions from the Databricks community. We are very excited to be working together with Databricks users to build greater ML applications.”
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 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.
dotData pioneered Automated Feature Engineering to accelerate and augment the process of building AI/ML models, to drive higher business value for the enterprise. dotData ingests raw business data and uses an AI-based engine to automatically discover meaningful insights and build ML-ready feature tables from relational, transactional, temporal, geo-locational, and text data. dotData’s scalable, flexible platform enables data scientists to discover and evaluate outstanding AI features; and empowers business intelligence professionals to add AI/ML models to their BI stacks and predictive analytics applications quickly and easily. Fortune 500 organizations around the world use dotData to accelerate their ML and AI development to drive higher business value.
dotData has been recognized as a leader by Forrester in the 2019 New Wave for AutoML platforms. dotData has also been recognized as the “best machine learning platform” for 2019 by the AI breakthrough awards; was named a CRN “emerging vendor to watch” in the big data space in 2019 and featured on CRN’s 2020 and 2021 Big Data 100 list; and was named to CB Insights’ Top 100 AI Startups in 2020. For more information, visit www.dotdata.com, and join the conversation on Twitter and LinkedIn.
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