dotData, a provider of end-to-end data science automation and operationalization, is releasing dotDataPy, a lightweight and scalable Python library that enables advanced users to access dotData’s data science automation functionality. 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).
Read the full article on Database Trends & Applications, which features Ryohei Fujimaki, dotData CEO and founder.
