This is for data scientists, data analysts, algorithm writers and projects managers.
Azure Machine-Learning WorkBench empowers Data-Science jobs at 2 levels: it centralizes codes, potentially in several programming languages, in one common interface and it allows to easily adapt the performance needs (Local, Docker, VM DataScience CPU, GPUs…).
We will first present fundamentals of the WorkBench and we will then show a demo on a real life project involving Data Preparation, Python Scripting in Jupyter, Web Service publication and environment switch.
Why I Want to Present This Session:
I would like to share my experiences on AML and WorkBench in order to show how it allows performance scalability and to save worldwide datascientists’ precious time.
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- Machine Learning : From Zero to Hero in one hour with AML WorkBench - March 20, 2018