![]() The Jupyter Notebook app enables us today to run on-premise interactive machine learning scenarios with ML.NET using C# or F# in a web browser, without bringing software or hardware costs. The list of language kernels supported by Jupyter -including Python, R, Julia, Matlab and many others- has been extended with. ![]() The Jupyter ecosystem provides such an interactive environment, and there’s good news for. Data scientists involved in data analysis, data preparation, and model training prefer a fully interactive environment that allows mixing content, live source code, and program output in a single (web) page that gives immediate feedback when the data or the code changes. In machine learning and other data-centric tasks this cycle creates so much overhead an delay that it simply doesn’t make sense. NET developers have been building classic console, desktop or web applications through a stop-modify-recompile-restart cycle.
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