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  • 19 October 2022
Cloud Platforms

Reliable and Reusable Python Script Sharing With KNIME

Agenda

In large organizations, higher productivity results from collaboration on preferred tools. Regardless of the tools your data team prefers, KNIME offers a solution for fast and productive collaboration! Python scripts can now be made available to all the KNIME users in the organization in a reliable and user-friendly way.

In the first part of this webinar, we will focus on a low-code approach. You will learn how to wrap Python scripts in the KNIME component, – KNIME’s native solution for sharing and reusing bundled functionalities.
In the second part of the webinar, we will go full code and show the Pythonists how to write KNIME nodes and extensions in Python. This new capability offers data scientists an easy coding framework to wrap existing and custom Python libraries, define node configuration, and execution, as well as dialog definition via a Pythonic API.
In this session you will learn:
  • How to build reliable and reusable components with Python scripts (low code)
  • How to write KNIME nodes and extensions completely in Python (full code)
  • To boost collaboration across the organization
  • To share your work with KNIME users in a friendly and effective way
Mahantesh Pattadkal
Mahantesh Pattadkal

Data Scientist at KNIME

Mahantesh Pattadkal has completed his Master’s in Digital Engineering from OVG Magdeburg University, and is currently working as a data scientist at KNIME. The data science techniques he is interested in are machine learning, natural language processing, deep learning, predictive modeling, and business analytics. He enjoys solving data science use cases with tools/languages like KNIME, Python, SQL, Tensorflow/Keras, Pytorch, and R.
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Paolo Tamagnini
Paolo Tamagnini

Data Science Evangelist at KNIME

Paolo Tamagnini is a data science evangelist at KNIME. After graduating with a master’s degree in data science at Sapienza University of Rome, Paolo gathered research experience at New York University in machine learning interpretability and visual analytics tools. Since working at KNIME, Paolo has presented different workshops in the USA and Europe, and developed a number of reusable guided analytics applications for automated machine learning and human-in-the-loop analytics.
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