Designed for Advanced Data Scientists, Enabling End-To-End Enterprise Data Science, Increased Flexibility and Productivity with a Few Lines of Python Code
dotData, the first and only company focused on delivering end-to-end data science automation and operationalization for the enterprise, announced the launch of dotDataPy, 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 ever before.
“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.”
dotData’s 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 outcomes.