Data science automation with
just a few lines of Python code
End-to-End Data Science Automation for Advanced Users
dotDataPy enables greater flexibility through its Python interface, and empowers data scientists to achieve higher productivity and drive greater business impact than ever before. It can be easily integrated with Jupyter notebooks and other Python development environments, enabling users to fully leverage the advanced Python ecosystem.
Increased Flexibility and Productivity to Solve More Challenges, Faster
dotDataPy is a rich 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 Python code.
Better Features, Deeper Insights
dotDataPy’s AI-powered feature engineering derives better features, faster to deliver deeper insights and higher predictability. It automatically transforms source tables with complex relationships into a single “feature” table and makes them ready for machine learning – typically exploring millions of features for predictive modeling.
Its proprietary AI-algorithm is built for big data, automating and streamlining the most manual, intuitional and time-consuming “black-arts” process of last-mile ETL and feature engineering while being scalable to process hundreds of tables from different sources and perform computations on billions of records.
The AI-driven features are transparent, visually explained in natural languages, and easily interpretable even for business users, accelerating implementations of data science in business operations with confidence and accountability. Moreover, they are fully data-centric and thus unbiased.Contact Us for A Demo
Highly Accuracy, Greater Transparency
dotDataPy’s automated machine learning conducts hundreds of trials to finely tune state-of-the-art machine learning algorithms (including proprietary ones) for the best accuracy in various optimization criteria. The fully-automated process frees up the time and resources of data scientists and enables BI engineers/business analysts to produce high-quality machine learning models, thus enabling teams to execute more data science projects than ever before.
The AutoML algorithm explores not only machine learning algorithms, but also feature preprocessing methods such as missing value imputation, outlier filtering, and normalization. In conjunction with AI-powered feature engineering, dotDataPy automates and streamlines the end-to-end data science process on a single environment as a one-stop-shop.Contact Us for a Demo
Easy Integration, Powerful Features
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).
The AI-powered features can be easily plugged into customized ML algorithms, enabling advanced users to further refine their models and extract new business insights.Contact Us for a Demo