January 30, 2019
is a lightweight and scalable Python library that enables advanced users
to access dotData's data science automation functionality, including
AI-powered feature engineering and automated machine learning. With just
a few lines of code, data scientists can now create, execute and
validate end-to-end data science pipelines.
dotDataPy can be easily integrated with Jupyter notebooks and other
Python development environments, enabling users to fully leverage the
advanced Python ecosystem, including rich visualization (e.g. Matplotlib
and Plotly), state-of-the-art machine learning/deep learning tools (e.g.
scikit-learn, Spark MLlib, PyTorch, and TensorFlow), and flexible
DataFrames (e.g. pandas and PySpark). dotDataPy enables greater
flexibility through its Python interface, and empowers data scientists
to achieve higher productivity and drive greater business impact than
"We are excited to announce dotDataPy, created specifically to help
advanced users accelerate their data science projects," said Ryohei
Fujimaki, Ph.D., CEO and founder of dotData. "Now, end-to-end data
science automation can be implemented with just a few lines of Python
code using dotDataPy, or with just a few clicks with dotData Platform,
giving data scientists the freedom to solve more challenges, faster.
AI-powered Data Science Automation Platform completely automates the
entire data science process, from data collection through
production-ready models, including feature engineering. As a result, the
entire data science process is accelerated from months to days, enabling
companies to rapidly scale their AI/ML initiatives to drive
transformative business changes.
The dotData Platform also democratizes the data science process by
enabling more participants with different skill levels to effectively
execute on projects, making it possible for enterprises to
operationalize 10x more projects with transparent and actionable
dotDataPy will generally be available March 2019.