Azure Machine Learning Services provide an integrated, end-to-end data science experience to prepare data, develop experiments, and deploy models at cloud scale by Microsoft.
Machine Learning with Azure Machine Learning Services
ML.NET
ML.NET (Machine Learning .NET) is a cross-platform, open-source Machine Learning Framework by Microsoft. We can use C# or F# to develop ML.NET applications. ML.NET is cross-platform and can run on Windows, Linux, and mac. It supports tasks like regression and classification, both training and consumption. It also supports core data types, extensible pipelines, data structures, tooling support, advanced performance math etc.
Machine Learning Studio
Machine Learning Studio provides a no-coding-required environment for developing ML experiments that, once verified as successful, can be associated to production applications via web service APIs.
ML Studio is actually the front-end for the Microsoft Azure Machine Learning service. It looks like Visual Studio tool (to create executable programs), but don’t carry the analogy too far. ML Studio has three primary working areas. on the left you can see items with names like Saved Datasets, Data Input and Output, and Machine Learning. These are categories, and if you expand any you see specific items that can be dragged onto the center design surface. This is somewhat similar to the Visual Studio Toolbox where you can drag UI controls onto a design surface. The center area of ML Studio is called the experiment. This is analogous to the Visual Studio editor — the place where you do most of your work. An experiment title is roughly analogous to a Visual Studio Solution name.
The right-hand side of ML Studio shows details about whatever is currently selected in the main work area.
Hosted notebooks
The notebooks VM based authoring is directly integrated into Azure Machine Learning, providing a code-first experience for Python developers to appropriately build and deploy models in the workspace experience. Data scientists can perform every operation supported by the Azure Machine Learning Python SDK using an accustomed Jupyter notebook in a secure, robust, enterprise-ready environment.
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