If you have access to SAP Data Intelligence, you can get started quicker as SAP Data Intelligence already has JupyterLab integrated. download a set of Jupyter Notebooks that have been prepared for you.install the libraries to your Python environment, which are needed to connect and push-down calculation and training logic to SAP HANA.have a Python development environment, preferably JupyterLab.have access to a SAP HANA system (version 2.0 SPS 03 or higher).Or if you just want to get an idea of the concept without getting hands-on, you can also just scroll through the Notebooks that are shared. You can implement the scenario yourself using your own SAP HANA instance. That’s right, leverage the power of SAP HANA without leaving your existing Python framework! #PYTHON JUPYTER NOTEBOOK TUTORIAL HOW TO#If you are already experienced with Machine Learning, you might be curious how to train ML models directly in SAP HANA from your preferred Python environment. In case you are not familiar with Machine Learning or Python, this project can be a starting point. Since SAP HANA contains predictive algorithms you can train ML models within SAP HANA on the existing information – without having to extract and duplicate the data! I like to call this the “push-down”. This data can also be a very valuable asset for Machine Learning tasks. If you are using SAP HANA, you probably have valuable business data in that system. Trigger predictive algorithms either from local Jupyter Notebooks or, even better, from Jupyter Notebooks within SAP Data Intelligence. With this tutorial you will learn how to train Machine Learning (ML) models in SAP HANA through Python code.
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