dotData Enterprise Version 2.0 GA
July 20, 2020
Enterprise Version 2.0 comes with significant updates and new features
that completely change the AI/ML experience for citizen data scientists
and deliver greater performance and transparency.
dotData released Version 2.0 as the Company continues to expand its
product offerings and scale up operations to meet growing market demand
for its full-cycle data science automation platform. In addition to full
UX remodeling of its interface, additional key updates that deliver
superior AI/ML experience include auto-balancing of accuracy and
transparency, more accurate and interpretable auto-designed features,
expanded out of the box connectivities and seamless model porting with
dotDataPy and dotData Stream.
"We take the quality of features and quality of outcomes very seriously
and are committed to continuous improvement of our platform," said
Ryohei Fujimaki, Ph.D., founder, and CEO of dotData. "After we released
dotData Enterprise 1.6.2 in December 2019, we made a big decision to
fully remodel our UX to realize our commitment of democratizing
enterprise data science. Today, we are very happy to announce the
release of dotData 2.0 which redefines the AI/ML experience for all.
This is a significant change that continues to expand our vision to
simplify the data science process so that BI, data professionals, and
analytics teams can accelerate the development of predictive analytics
by taking advantage of AI and data science using automated machine
Key updates of dotData Enterprise Version 2.0 include:
Full UX remodeling - Significantly simplified interface and workflow
enable BI professionals to execute the entire ML/AI development process
in just five minutes and more powerful visualizations enhance feature
and model transparency.
Auto-balancing of accuracy and interpretability in AutoML - Automates
the process to explore simpler ML models with minimal change in
accuracy, in addition to pursuing the model for the highest accuracy.
This enables users to balance accuracy and interpretability based on
their business requirements.
More accurate and interpretable features - Version 2.0 introduces
various improvements in its AI-powered feature engineering to produce
more accurate and more interpretable features such as advanced
categorical encoding, more natural feature explanation, and more robust
feature selection to avoid multicollinearity.
Seamless BI+AI experience - Is realized through out-of-box
connectivities with third-party data and business Intelligence platforms
including Tableau, Teradata, and MS SQL / Azure database.
Flexible deployment options via dotDataPy and dotData Stream - Features
and models developed using Version 2.0 are deployable both on dotDataPy
as customizable Python end-points and on dotData Stream as real-time
dotData provides AutoML 2.0 solutions that help accelerate the process
of developing AI and Machine Learning models for use in predictive
analytics BI dashboards and advanced analytics applications. dotData
makes it easy for BI developers and data engineers to develop AI/ML
models in just days by automating the full life-cycle of the data
science process, from business raw data through feature engineering to
implementation of ML in production utilizing its proprietary AI
technologies. dotData's AI-powered feature engineering automatically
applies data transformation, cleansing, normalization, aggregation, and
combination, and transforms hundreds of tables with complex
relationships and billions of rows into a single feature table,
automating the most manual data science projects that are fundamental to
developing predictive analytics solutions.
democratizes data science by enabling BI developers and data engineers
to make enterprise data science scalable and sustainable. dotData
automates up to 100 percent of the AI/ML development workflow, enabling
users to connect directly to their enterprise data sources to discover
and evaluate millions of features from complex table structures and huge
data sets with minimal user input. dotData is also designed to
operationalize AI/ML models by producing both feature and ML scoring
pipelines in production, which IT teams can then immediately integrate
with business workflows. This can further automate the time-consuming
and arduous process of maintaining the deployed pipeline to ensure
repeatability as data changes over time. With the dotData GUI, AI/ML
development becomes a five-minute operation, requiring neither
significant data science experience nor SQL/Python/R coding.