|Azure Machine Learning Enhancements|
|Written by Janet Swift|
|Wednesday, 27 September 2017|
Three new strands to Azure Machine Learning (AML) were launched at Microsoft Ignite this week. Also announced was the integration of AML with Excel bringing AI functions to spreadsheets.
Heading the list of launches is the Azure Machine Learning Workbench which is described as a cross-platform client for AI-powered data wrangling and experiment management. It serves as a control panel for development lifecycle and helps new users to get started with AML services by the inclusion of samples:
Workbench is open, extensible and flexible, allowing developers and data scientists to author models in Python, PySpark and Scala. It supports integration with Jupyter Notebooks and with popular IDEs, including Visual Studio Code and PyCharm.
Azure Machine Learning Workbench includes a data wrangling tool to help with data preparation that lets you build sdata transformations by example. On the Azure blog, Matt Winkler writes:
We have combined a variety of techniques, using advanced research from Microsoft Research on program synthesis (PROSE) and data cleaning, to create a data wrangling experience that drastically reduces the time that needs to be spent getting data prepared. With the inclusion of a simple set of libraries for handling data sources, data scientists can focus on their code, not on changing file paths and dependencies when they move between environments. By building these experiences together, the data scientist can leverage the same tools in the small and in the large, as they scale out transparently across our cloud compute engines, simply by choosing target environments for execution.
Two new AML services were launched:
The other news was of the integration of AML with Excel allowing Excel users to deploy cloud hosted AI functions in their spreadsheets. Typing =AZUREML in a cell will access a list of the AI functions that have already been available to Data Scientists as part of AML.
Using these functions it is possible to create models customized for your needs. In the example below an analyst is using an Azure ML function to analyze land use. Behind the function is a custom built deep neural network classifier for satellite imagery running in Azure.
Reiterating the announcements on the Azure blog, Joseph Sirosh, Microsoft's Corporate Vice President, Artificial Intelligence & Research says that they:
demonstrate our mission to bring AI to every developer and every organization on the planet, and to help businesses augment human ingenuity in unique and differentiated ways.
With AI becoming ever more important that fact that Microsoft is for lowering the barrier to incorporating AI into data-driven projects is very welcome.
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|Last Updated ( Wednesday, 27 September 2017 )|